Integration of AI-Object Recognition in the automated audio file search process:
After setting up the initial interface for the freesound.org API and confirming everything works with test tags and basic search filters, the next major milestone is now in motion: AI-based object recognition using the GeminAI API.
The idea is to feed in an image (or a batch of them), let the AI detect what’s in it, and then use those recognized tags to trigger an automated search for corresponding sounds on freesound.org. The integration already loads the detected tags into an array, which is then automatically passed on to the sound search. This allows the system to dynamically react to the content of an image and search for matching audio files — no manual tagging needed anymore.
So far, the detection is working pretty reliably for general categories like “bird”, “car”, “tree”, etc. But I’m looking into models or APIs that offer more fine-grained recognition. For instance, instead of just “bird”, I’d like it to say “sparrow”, “eagle”, or even specific songbird species if possible. This would make the whole sound mapping feel much more tailored and immersive.
A list of test images will be prepared, but there’s already a testing matrix for different objects, situations, scenery and technical differences
On the freesound side, I’ve got the basic query parameters set up: tag search, sample rate, file type, license, and duration filters. There’s room to expand this with additional parameters like rating, bit depth, and maybe even a random selection toggle to avoid repetition when the same tag comes up multiple times.
Coming up: I’ll be working on whether to auto-play or download the selected audio files, and starting to test how the AI-generated tags influence the mood and quality of the soundscape. The long-term plan includes layering sounds, adjusting volumes, experimenting with EQ and filtering — all to make the playback more natural and immersive.
The NIME 2024 paper Interactive Sonification of 3D Swarmalators by Pedro Lucas et al.—a project that merges swarm intelligence with sound and music systems in an unusual and intriguing way. Their work explores what happens when coupled oscillators (called “Swarmalators”) move in 3D space and interact through both spatial and phase dynamics, resulting in emergent sonic behavior.
What I Found Fascinating
First, the concept of “sound swarming” is compelling. Each swarmalator acts as a tiny sound generator (an oscillator), and together they form a swarm that evolves over time. As the swarm grows or changes state, the collective sonic output transforms, producing emergent, ambient textures. It’s like a synthetic ecology where sonic patterns ripple through space and time.
I really appreciated the balance between individual control (through the interactive swarmalator) and system-level complexity. The way one agent—controlled by a user—can gently nudge the entire swarm toward a new sound state (syncing phases or shifting spatial positions) reminds me of soft systems thinking, or how small disturbances in dynamic environments can guide large-scale changes. It’s a musical metaphor for influence and emergence.
Also interesting: the decision to use 3D space—not just as visual flair, but as a functional parameter in the sound synthesis. The angle between an agent’s position and the swarm center is mapped to modulation (LFO phase), which adds spatial logic to the sonic texture. This connection between location, rhythm, and pitch expands the expressive range of the system without overwhelming the user with complexity.
Critique or Question…
While I found the system architecture well thought out (especially the modular design between Unity and Max), I do wonder how accessible the musical outcomes really are for performers or audiences who aren’t already embedded in experimental sound practices.
What does “sound swarming” feel like to someone who isn’t reading the underlying equations?There’s an assumption that emergent sonic behavior is interesting in itself—which is often true—but I’d be curious about perceived musicality or narrative structure. How does the user know when something meaningful is happening?
How intuitive is the control? The interactive swarmalator is a smart concept, but its influence seems subtle and potentially slow. In a live performance context, would that control feel satisfying? Or would it feel like poking a beehive and waiting to see what happens?
Sonification or Composition? I’m torn between seeing this as a sonification project (data → sound) or a compositional tool. It seems to sit between both, but I’d love to see clearer articulation on whether the goal is to represent something through sound, or to compose emergent music through interaction.
What I Would Like to Explore Further
This system opens a door to interesting possibilities for multisensory representation, especially when combined with haptics or extended reality (which the authors mention as future work). Imagine if you could feel vibrations from nearby swarmalators, or use your hand in an AR space to guide sound clusters around you.
It also made me think about accessibility: how could this system be made tangible for someone who doesn’t rely on visual interfaces? Could you “hear” the swarm’s shape or “feel” its convergence? Maybe adding another sensory layer could help bridge that gap.
Finally, I’d love to see this concept applied to non-musical data—for example, using environmental or physiological data as inputs to control the swarm behavior. That could transform this into an ambient, perceptual feedback tool rather than just a sound art piece.
This paper definitely broadened how I think about interactive systems, sonic feedback, and emergence. While the sonic aesthetics may lean toward experimental music, the design principles offer insight into how complex systems can be explored through sound—not just explained, but felt.
Reference:
P. Lucas, S. Fasciani, A. Szorkovszky, and K. Glette, “Interactive Sonification of 3D Swarmalators,” in Proc. Int. Conf. New Interfaces for Musical Expression (NIME), Utrecht, The Netherlands, Sep. 2024. [Online]. Available: https://doi.org/10.5281/zenodo.10948289
Tests on automated audio file search via freesound.org api:
For further use in the automated audio file search of the recognized objects I tested the freesound.org api and programmed the first interface for testing purposes. The first thing I had to do was request an API-Key by freesound.org. After that I noticed an interesting point to think about using it in my project: it is open for 5000 requests per year, but I will research on possibilities for using it more. For the testing 5000 is more than enough.
The current code already searches with a few testing tags and gives possibilities to filter the searches by samplerate, duration, licence and file type. There might be added more filter possibilities next like rating, bit depth, and maybe the possibility of random file selection so it won’t be always the same for each tag.
Next steps would also include to either download the file or just play it automatically. Then there will be tests on using the tags of the AI image recognition code for this automated search. And later in the process I have to figure out the playback of multiple files, volume staging and filtering or EQing methods for masking effects etc…
Test gui for automated sound searching via freesounds.org API
Research on sonification of images / video material and different approaches – focus on RGB
The paper by Kopecek and Ošlejšek presents a system that enables visually impaired users to perceive color images through sound using a semantic color model. Each primary color (such as red, green, or blue) is assigned a unique sound, and colors in an image are approximated by the two closest primary colors. These are represented through two simultaneous tones, with volume indicating the proportion of each color. Users can explore images by selecting pixels or regions using input devices like a touchscreen or mouse. The system calculates the average color of the selected area and plays the corresponding sounds. Distinct audio cues indicate image boundaries, and sounds can be either synthetic or instrument-based, with timbre and pitch helping to differentiate them. Users can customize colors and sounds for a more personalized experience. This approach allows for dynamic, efficient exploration of images and supports navigation via annotated SVG formats.
image seperation by Kopecek and Ošlejšek
The review by Sarkar, Bakshi, and Sa offers an overview of various image sonification methods designed to help visually impaired users interpret visual scenes through sound. It covers techniques such as raster scanning, query-based, and path-based approaches, where visual data like pixel intensity and position are mapped to auditory cues. Systems like vOICe and NAVI use high and low-frequency tones to represent image regions vertically. The paper emphasizes the importance of transfer functions, which link image properties to sound attributes such as pitch, volume, and frequency. Different rendering methods—like audification, earcons, and parameter mapping—are discussed in relation to human auditory perception. Special attention is given to color sonification, including the semantic color model introduced by Kopecek and Ošlejšek, which improves usability through clearly distinguishable tones. The paper also explores applications in fields such as medical imaging, algorithm visualization, and network analysis, and briefly touches on sound-to-image conversions.
Principles of the image-to-sound mapping
Matta, Rudolph, and Kumar propose the theoretical system “Auditory Eyes,” which converts visual data into auditory and tactile signals to support blind users. The system comprises three main components: an image encoder that uses edge detection and triangulation to estimate object location and distance; a mapper that translates features like motion, brightness, and proximity into corresponding sound and vibration cues; and output generators that produce sound using tools like Csound and tactile feedback via vibrations. Motion is represented using effects like Doppler shift and interaural time difference, while spatial positioning is conveyed through head-related transfer functions. Brightness is mapped to pitch, and edges are conveyed through tone duration. The authors emphasize that combining auditory and tactile information can create a richer and more intuitive understanding of the environment, making the system potentially very useful for real-world navigation and object recognition.
References
Kopecek, Ivan, and Radek Ošlejšek. 2008. “Hybrid Approach to Sonification of Color Images.” In Third 2008 International Conference on Convergence and Hybrid Information Technology, 721–726. IEEE. https://doi.org/10.1109/ICCIT.2008.152.
Sarkar, Rajib, Sambit Bakshi, and Pankaj K Sa. 2012. “Review on Image Sonification: A Non-visual Scene Representation.” In 1st International Conference on Recent Advances in Information Technology (RAIT-2012), 1–5. IEEE. https://doi.org/10.1109/RAIT.2012.6194495.
Matta, Suresh, Heiko Rudolph, and Dinesh K Kumar. 2005. “Auditory Eyes: Representing Visual Information in Sound and Tactile Cues.” In Proceedings of the 13th European Signal Processing Conference (EUSIPCO 2005), 1–5. Antalya, Turkey. https://www.researchgate.net/publication/241256962.
Expanded research on sonification of images / video material and different approaches:
Yeo and Berger (2005) write in “A Framework for Designing Image Sonification Methods” about the challenge of mapping static, time-independent data like images into the time-dependent auditory domain. They introduce two main concepts: scanning and probing. Scanning follows a fixed, pre-determined order of sonification, whereas probing allows for arbitrary, user-controlled exploration. The paper also discusses the importance of pointers and paths in defining how data is mapped to sound. Several sonification techniques are analyzed, including inverse spectrogram mapping and the method of raster scanning (which already was explained in the Prototyping I – Blog entry), with examples illustrating their effectiveness. The authors suggest that combining scanning and probing offers a more comprehensive approach to image sonification, allowing for both global context and local feature exploration. Future work includes extending the framework to model human image perception for more intuitive sonification methods.
Time on “perpendicular” axis. (Yeo, Berger, 2005)Raster scanning method (Yeo, Berger, 2005)Pointers in different shapes: (a) single point, (b) line/curve, (c) area, and (d) set of distributed points. (Yeo, Berger, 2005)Inverse spectrogram scanning (Yeo, Berger, 2005)
Sharma et al. (2017) explore action recognition in still images using Natural Language Processing (NLP) techniques in “Action Recognition in Still Images Using Word Embeddings from Natural Language Descriptions.” Rather than training visual action detectors, they propose detecting prominent objects in an image and inferring actions based on object relationships. The Object-Verb-Object (OVO) triplet model predicts verbs using object co-occurrence, while word2vec captures semantic relationships between objects and actions. Experimental results show that this approach reliably detects actions without computationally intensive visual action detectors. The authors highlight the potential of this method in resource-constrained environments, such as mobile devices, and suggest future work incorporating spatial relationships and global scene context.
Iovino et al. (1997) discuss developments in Modalys, a physical modeling synthesizer based on modal synthesis, in “Recent Work Around Modalys and Modal Synthesis.” Modalys allows users to create virtual instruments by defining physical structures (objects), their interactions (connections), and control parameters (controllers). The authors explore the musical possibilities of Modalys, emphasizing its flexibility and the challenges of controlling complex synthesis parameters. They propose applications such as virtual instrument construction, simulation of instrumental gestures, and convergence of signal and physical modeling synthesis. The paper also introduces single-point objects, which allow for spectral control of sound, bridging the gap between signal synthesis and physical modeling. Real-time control and expressivity are emphasized, with future work focused on integrating Modalys with real-time platforms.
McGee et al. (2012) describe Voice of Sisyphus, a multimedia installation that sonifies a black-and-white image using raster scanning and frequency domain filtering in “Voice of Sisyphus: An Image Sonification Multimedia Installation.” Unlike traditional spectrograph-based sonification methods, this project focuses on probing different image regions to create a dynamic audio-visual composition. Custom software enables real-time manipulation of image regions, polyphonic sound generation, and spatialization. The installation cycles through eight phrases, each with distinct visual and auditory characteristics, creating a continuous, evolving experience. The authors discuss balancing visual and auditory aesthetics, noting that visually coherent images often produce noisy sounds, while abstract images yield clearer tones. The project draws inspiration from early experiments in image sonification and aims to create a synchronized audio-visual experience engaging viewers on multiple levels.
Software Interface for Voice of Sisyphus (McGee et al., 2012)
Roodaki et al. (2017) introduce SonifEye, a system that uses physical modeling sound synthesis to convey visual information in high-precision tasks, in “SonifEye: Sonification of Visual Information Using Physical Modeling Sound Synthesis.” They propose three sonification mechanisms: touch, pressure, and angle of approach, each mapped to sounds generated by physical models (e.g., tapping on a wooden plate or plucking a string). The system aims to reduce cognitive load and avoid alarm fatigue by using intuitive, natural sounds. Two experiments compare the effectiveness of visual, auditory, and combined feedback in high-precision tasks. Results show that auditory feedback alone can improve task performance, particularly in scenarios where visual feedback may be distracting. The authors suggest applications in medical procedures and other fields requiring precise manual tasks.
Dubus and Bresin review mapping strategies for the sonification of physical quantities in “A Systematic Review of Mapping Strategies for the Sonification of Physical Quantities.” Their study analyzes 179 publications to identify trends and best practices in sonification. The authors find that pitch is the most commonly used auditory dimension, while spatial auditory mapping is primarily applied to kinematic data. They also highlight the lack of standardized evaluation methods for sonification efficiency. The paper proposes a mapping-based framework for characterizing sonification and suggests future work in refining mapping strategies to enhance usability.
References
Yeo, Woon Seung, and Jonathan Berger. 2005. “A Framework for Designing Image Sonification Methods.” In Proceedings of ICAD 05-Eleventh Meeting of the International Conference on Auditory Display, Limerick, Ireland, July 6-9, 2005.
Sharma, Karan, Arun CS Kumar, and Suchendra M. Bhandarkar. 2017. “Action Recognition in Still Images Using Word Embeddings from Natural Language Descriptions.” In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), 978-1-5090-4941-7/17. DOI: 10.1109/WACVW.2017.17.
Iovino, Francisco, Rene Causse, and Richard Dudas. 1997. “Recent Work Around Modalys and Modal Synthesis.” In Proceedings of the International Computer Music Conference (ICMC).
McGee, Ryan, Joshua Dickinson, and George Legrady. 2012. “Voice of Sisyphus: An Image Sonification Multimedia Installation.” In Proceedings of the 18th International Conference on Auditory Display (ICAD-2012), Atlanta, USA, June 18–22, 2012.
Roodaki, Hessam, Navid Navab, Abouzar Eslami, Christopher Stapleton, and Nassir Navab. 2017. “SonifEye: Sonification of Visual Information Using Physical Modeling Sound Synthesis.” IEEE Transactions on Visualization and Computer Graphics 23, no. 11: 2366–2371. DOI: 10.1109/TVCG.2017.2734320.
Dubus, Gaël, and Roberto Bresin. 2013. “A Systematic Review of Mapping Strategies for the Sonification of Physical Quantities.” PLoS ONE 8(12): e82491. DOI: 10.1371/journal.pone.0082491.
Shift of intention of the project due to time plan:
By narrowing down the topic to ensure the feasibility of this project the focus or main purpose of the project will be the artistic approach. The tool will still combine the use of direct image to audio translation and the translation via sonification into a more abstract form. The main use cases will be generating unique audio samples for creative applications, such as sound design for interactive installations, brand audio identities, or matching image soundscapes and the possibility to be a versatile instrument for experimental media artists and display tool for image information.
By further research on different possibilities of sonification of image data and development of the sonification language itself the translation and display purpose is going to get more clear within the following weeks.
Testing of Google Gemini API for AI Object and Image Recognition:
The first testing of the Google Gemini Api started well. There are different models for dedicated object recognition and image recognition itself which can be combined to analyze pictures in terms of objects and partly scenery. These models (SSD, EfficientNET,…) create similar results but not always the same. It might be an option to make it selectable for the user (so that in a failure case a different model can be tried and may give better results). The scenery recognition itself tends to be a problem. It may be a possibility to try out different apis.
The data we get from this AI model is a tag for the recognized objects or image content and a percentage of the probability.
The next steps for the direct translation of it into realistic sound representations will be to test the possibility of using the api of freesound.org to search directly and automated for the recognized object tags and load matching audio files. These search calls also need to filter by copyright type of the sounds and a choosing rule / algorithm needs to be created.
object recognition: efficient float 16 model (Photo by Jason Oh on unsplash)object recognition: image splice test – recognition fail (Photo by Jason Oh on unsplash)object recognition: accurate but low score (Photo: https://lernen.zoner.de/)object recognition (photo: zdf.de)
Research on sonification of images / video material and different approaches:
The world of image sonification is rich with diverse techniques, each offering unique ways to transform visual data into auditory experiences. The world of image sonification is rich with diverse techniques, each offering unique ways to map visual data into auditory experiences. One of the most straightforward methods is raster scanning, introduced by Yeo and Berger. This technique maps the brightness values of grayscale image pixels directly to audio samples, creating a one-to-one correspondence between visual and auditory data. By scanning an image line by line, from top to bottom, the system generates a sound that reflects the texture and patterns of the image. For example, a smooth gradient might produce a steady tone, while a highly textured image could result in a more complex, evolving soundscape. The process is fully reversible, allowing for both image sonification and sound visualization, making it a versatile tool for artists and researchers alike. This method is particularly effective for sonifying image textures and exploring the auditory representation of visual filters, such as “patchwork” or “grain” effects.(Yeo and Berger, 2006)
Principle raster scanning (Yeo and Berger, 2006)
In contrast, Audible Panorama (Huang et al. 2019) automates sound mapping for 360° panorama images used in virtual reality (VR). It detects objects using computer vision, estimates their depth, and assigns spatialized audio from a database. For example, a car might trigger engine sounds, while a person generates footsteps, creating an immersive auditory experience that enhances VR realism. A user study confirmed that spatial audio significantly improves the sense of presence. It contains a interesting concept regarding to choosing a random audio file from a sound library to avoid producing similar or same results. Also it mentions the aspect of postprocessing the audios which also would be a relevant aspect for the image extender project.
principle audible panorama (Huang et al. 2019)
Another approach, HindSight (Schoop, Smith, and Hartmann 2018), focuses on real-time object detection and sonification in 360° video. Using a head-mounted camera and neural networks, it detects objects like cars and pedestrians, then sonifies their position and danger level through bone conduction headphones. Beeps increase in tempo and pan to indicate proximity and direction, providing real-time safety alerts for cyclists.
Finally, Sonic Panoramas (Kabisch, Kuester, and Penny 2005) takes an interactive approach, allowing users to navigate landscape images while generating sound based on their position. Edge detection extracts features like mountains or forests, mapping them to dynamic soundscapes. For instance, a mountain ridge might produce a resonant tone, while a forest creates layered, chaotic sounds, blending visual and auditory art. It also mentions different approaches for sonification itself. For example the idea of using micro (timbre, pitch and melody) and macro level (rhythm and form) mapping.
principle sonic panoramas (Kabisch, Kuester, and Penny 2005)
Each of these methods—raster scanning, Audible Panorama, HindSight, and Sonic Panoramas—demonstrates the versatility of sonification as a tool for transforming visual data into sound and lead keeping these different approaches in mind for developing my own sonification language or mapping method. It also leads to further research by checking some useful references they used in their work for a deeper understanding of sonification and extending the possibilities.
References
Huang, Haikun, Michael Solah, Dingzeyu Li, and Lap-Fai Yu. 2019. “Audible Panorama: Automatic Spatial Audio Generation for Panorama Imagery.” In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–11. Glasgow, Scotland: ACM. https://doi.org/10.1145/3290605.3300851.
Kabisch, Eric, Falko Kuester, and Simon Penny. 2005. “Sonic Panoramas: Experiments with Interactive Landscape Image Sonification.” In Proceedings of the 2005 International Conference on Artificial Reality and Telexistence (ICAT), 156–163. Christchurch, New Zealand: HIT Lab NZ.
Schoop, Eldon, James Smith, and Bjoern Hartmann. 2018. “HindSight: Enhancing Spatial Awareness by Sonifying Detected Objects in Real-Time 360-Degree Video.” In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12. Montreal, QC, Canada: ACM. https://doi.org/10.1145/3173574.3173717.
Yeo, Woon Seung, and Jonathan Berger. 2006. “Application of Raster Scanning Method to Image Sonification, Sound Visualization, Sound Analysis and Synthesis.” In Proceedings of the 9th International Conference on Digital Audio Effects (DAFx-06), 311–316. Montreal, Canada: DAFx.
The Image Extender project bridges accessibility and creativity, offering an innovative way to perceive visual data through sound. With its dual-purpose approach, the tool has the potential to redefine auditory experiences for diverse audiences, pushing the boundaries of technology and human perception.
The project is designed as a dual-purpose tool for immersive perception and creative sound design. By leveraging AI-based image recognition and sonification algorithms, the tool will transform visual data into auditory experiences. This innovative approach is intended for:
1. Visually Impaired Individuals 2. Artists and Designers
The tool will focus on translating colors, textures, shapes, and spatial arrangements into structured soundscapes, ensuring clarity and creativity for diverse users.
Core Functionality: Translating image data into sound using sonification frameworks and AI algorithms.
Target Audiences: Visually impaired users and creative professionals.
Platforms: Initially desktop applications with planned mobile deployment for on-the-go accessibility.
User Experience: A customizable interface to balance complexity, accessibility, and creativity.
Working Hypotheses and Requirements
Hypotheses:
Cross-modal sonification enhances understanding and creativity in visual-to-auditory transformations.
Intuitive soundscapes improve accessibility for visually impaired users compared to traditional methods.
Requirements:
Develop an intuitive sonification framework adaptable to various images.
Integrate customizable settings to prevent sensory overload.
Ensure compatibility across platforms (desktop and mobile).
Subtasks
1. Project Planning & Structure
Define Scope and Goals: Clarify key deliverables and objectives for both visually impaired users and artists/designers.
Research Methods: Identify research approaches (e.g., user interviews, surveys, literature review).
Project Timeline and Milestones: Establish a phased timeline including prototyping, testing, and final implementation.
Identify Dependencies: List libraries, frameworks, and tools needed (Python, Pure Data, Max/MSP, OSC, etc.).
2. Research & Data Collection
Sonification Techniques: Research existing sonification methods and metaphors for cross-modal (sight-to-sound) mapping and research different other approaches that can also blend in the overall sonification strategy.
Psychoacoustics & Perceptual Mapping: Review how different sound frequencies, intensities, and spatialization affect perception.
Existing Tools & References: Study tools like Melobytes, VOSIS, and BeMyEyes to understand features, limitations, and user feedback.
object detection from python yolo library
3. Concept Development & Prototyping
Develop Sonification Mapping Framework: Define rules for mapping visual elements (color, shape, texture) to sound parameters (pitch, timbre, rhythm).
Simple Prototype: Create a basic prototype that integrates:
AI content recognition (Python + image processing libraries).
Sound generation (Pure Data or Max/MSP).
Communication via OSC (e.g., using Wekinator).
Create or collect Sample Soundscapes: Generate initial soundscapes for different types of images (e.g., landscapes, portraits, abstract visuals).
example of puredata with rem library (image to sound in pure data by Artiom Constantinov)
4. User Experience Design
UI/UX Design for Desktop:
Design intuitive interface for uploading images and adjusting sonification parameters.
Mock up controls for adjusting sound complexity, intensity, and spatialization.
Accessibility Features:
Ensure screen reader compatibility.
Develop customizable presets for different levels of user experience (basic vs. advanced).
Mobile Optimization Plan:
Plan for responsive design and functionality for smartphones.
5. Testing & Feedback Collection
Create Testing Scenarios:
Develop a set of diverse images (varying in content, color, and complexity).
Usability Testing with Visually Impaired Users:
Gather feedback on the clarity, intuitiveness, and sensory experience of the sonifications.
Identify areas of overstimulation or confusion.
Feedback from Artists/Designers:
Assess the creative flexibility and utility of the tool for sound design.
Iterate Based on Feedback:
Refine sonification mappings and interface based on user input.
6. Implementation of Standalone Application
Develop Core Application:
Integrate image recognition with sonification engine.
Implement adjustable parameters for sound generation.
Error Handling & Performance Optimization:
Ensure efficient processing for high-resolution images.
Handle edge cases for unexpected or low-quality inputs.
Cross-Platform Compatibility:
Ensure compatibility with Windows, macOS, and plan for future mobile deployment.
7. Finalization & Deployment
Finalize Feature Set:
Balance between accessibility and creative flexibility.
Ensure the sonification language is both consistent and adaptable.
Documentation & Tutorials:
Create user guides for visually impaired users and artists.
Provide tutorials for customizing sonification settings.
Deployment:
Package as a standalone desktop application.
Plan for mobile release (potentially a future phase).
Technological Basis Subtasks:
Programming: Develop core image recognition and processing modules in Python.
Sonification Engine: Create audio synthesis patches in Pure Data/Max/MSP.
Integration: Implement OSC communication between Python and the sound engine.
UI Development: Design and code the user interface for accessibility and usability.
Testing Automation: Create scripts for automating image-sonification tests.
Possible academic foundations for further research and work:
Chatterjee, Oindrila, and Shantanu Chakrabartty. “Using Growth Transform Dynamical Systems for Spatio-Temporal Data Sonification.” arXiv preprint, 2021.
Chion, Michel. Audio-Vision. New York: Columbia University Press, 1994.
Ziemer, Tim. Psychoacoustic Music Sound Field Synthesis. Cham: Springer International Publishing, 2020.
Ziemer, Tim, Nuttawut Nuchprayoon, and Holger Schultheis. “Psychoacoustic Sonification as User Interface for Human-Machine Interaction.” International Journal of Informatics Society, 2020.
Ziemer, Tim, and Holger Schultheis. “Three Orthogonal Dimensions for Psychoacoustic Sonification.” Acta Acustica United with Acustica, 2020.
The project would be a program that uses either AI-content recognition or a specific sonification algorithm by using equivalent of the perception of sight (cross-model metaphors).
examples of cross modal metaphors (Görne, 2017, S.53)
This approach could serve two main audiences:
1. Visually Impaired Individuals: The tool would provide an alternative to traditional audio descriptions, aiming instead to deliver a sonic experience that evokes the ambiance, spatial depth, or mood of an image. Instead of giving direct descriptive feedback, it would use non-verbal soundscapes to create an “impression” of the scene, engaging the listener’s perception intuitively. Therefore, the aspect of a strict sonification language might be a good approach. Maybe even better than just displaying the sounds of the images. Or maybe a mixture of both.
2. Artists and Designers: The tool could generate unique audio samples for creative applications, such as sound design for interactive installations, brand audio identities, or cinematic soundscapes. By enabling the synthesis of sound based on visual data, the tool could become a versatile instrument for experimental media artists.
Purpose
The core purpose would be the mixture of both purposes before, a tool that supports and helps creating in the same suite.
The dual purpose of accessibility and creativity is central to the project’s design philosophy, but balancing these objectives poses a challenge. While the tool should serve as a robust aid for visually impaired users, it also needs to function as a practical and flexible sound design instrument.
The final product can then be used by people who benefit from the added perception they get of images and screens and for artists or designers as a tool.
Primary Goal
A primary goal is to establish a sonification language that is intuitive, consistent, and adaptable to a variety of images and scenes. This “language” would ideally be flexible enough for creative expression yet structured enough to provide clarity for visually impaired users. Using a dynamic, adaptable set of rules tied to image data, the tool would be able to translate colors, textures, shapes, and contrasts into specific sounds.
To make the tool accessible and enjoyable, careful attention needs to be paid to the balance of sound complexity. Testing with visually impaired individuals will be essential for calibrating the audio to avoid overwhelming or confusing sensory experiences. Adjustable parameters could allow users to tailor sound intensity, frequency, and spatialization, giving them control while preserving the underlying sonification framework. It’s important to focus on realistic an achievable goal first.
planning on the methods (structure)
research and data collection
simple prototyping of key concept
testing phases
implementation in an standalone application
ui design and mobile optimization
The prototype will evolve in stages, with usability testing playing a key role in refining functionality. Early feedback from visually impaired testers will be invaluable in shaping how soundscapes are structured and controlled. Incorporating adjustable settings will likely be necessary to allow users to customize their experience and avoid potential overstimulation. However, this customization could complicate the design if the aim is to develop a consistent sonification language. Testing will help to balance these needs
Initial development will target desktop environments, with plans to expand to smartphones. A mobile-friendly interface would allow users to access sonification on the go, making it easier to engage with images and scenes from any device.
In general, it could lead to a different perception of sound in connection with images or visuals.
Needed components
Technological Basis:
Programming Language & IDE: The primary development of the image recognition could be done in Python, which offers strong libraries for image processing, machine learning, and integration with sound engines. Also wekinator could be a good start for the communication via OSC for example.
Sonification Tools: Pure Data or Max/MSP are ideal choices for creating the audio processing and synthesis framework, as they enable fine-tuned audio manipulation. These platforms can map visual data inputs (like color or shape) to sound parameters (such as pitch, timbre, or rhythm).
Testing Resources: A set of test images and videos will be required to refine the tool’s translations across various visual scenarios.
Existing Inspirations and References:
– Melobytes: Software that converts images to music, highlighting the potential for creative auditory representations of visuals.
– VOSIS: A synthesizer that filters visual data based on grayscale values, demonstrating how sound synthesis can be based on visual texture.
– image-sonification.vercel.app: A platform that creates audio loops from RGB values, showing how color data can be translated into sound.
– BeMyEyes: An app that provides auditory descriptions for visually impaired users, emphasizing the importance of accessibility in technology design.
Academic Foundations:
Literature on sonification, psychoacoustics, and synthesis will support the development of the program. These fields will help inform how sound can effectively communicate complex information without overwhelming the listener.
Sound is more than a medium for communication—it’s a profound tool for conveying meaning, evoking emotions, and guiding interaction. Two critical concepts in this domain, Perception, Cognition and Action in Auditory Displays and Sonic Interaction Design (SID), illustrate the potential of sound to transform user experiences. Let’s dive into these fascinating dimensions and explore how they enrich interaction design.
The world of sound is intricate, with perception playing a central role in translating acoustic signals into meaning. Chapter 4 of The Sonification Handbook emphasizes the interplay between low-level auditory dimensions (pitch, loudness, timbre) and higher-order cognitive processes.
1. Multidimensional Sound Mapping: Designers often map data variables to sound dimensions. For instance: • Pitch represents stock price fluctuations. • Loudness indicates proximity to thresholds.
2. Dimensional Interaction: These mappings aren’t always independent. For example, a rising pitch combined with falling loudness can distort perceptions, leading users to overestimate changes.
3. Temporal and Spatial Cues: Sound’s inherent temporal qualities make it ideal for monitoring processes and detecting anomalies. Spatialized sound, like binaural audio, enhances virtual environments by creating immersive experiences.
The Human Connection
What sets auditory displays apart is their alignment with human cognition: • Auditory Scene Analysis: Our brains can isolate sound streams (a melody amidst noise). • Action and Perception Loops: Interactive displays that let users modify sounds in real-time (tapping to control rhythm) leverage embodied cognition, connecting users’ actions to auditory feedback.
Sonic Interaction Design: Designing for Engagement
SID extends the principles of auditory perception into the realm of interaction. It focuses on creating systems where sound is an active, responsive participant in user interaction. This isn’t about adding sound arbitrarily; it’s about making sound integral to the product experience.
Core Concepts:
1. Closed-Loop Interaction: Users generate sound through actions, which then guide their behavior. Think of a rowing simulator where audio feedback helps athletes fine-tune their movements.
2. Multisensory Design: SID integrates sound with visual, tactile, and proprioceptive cues, ensuring a cohesive experience. For example, the iPod’s click wheel creates a pseudo-haptic illusion through auditory feedback.
3. Natural Sounds vs. Arbitrary Feedback: Research shows users prefer natural, intuitive sound interactions—like the “clickety-clack” of a spinning top model—over abstract sounds.
Aesthetic and Emotional Dimensions
Sound isn’t just functional; it’s deeply emotional: • Pleasantness and Annoyance: Sounds that align with user expectations can make interactions enjoyable, while poorly designed sounds risk irritation. • Emotional Resonance: Artifacts like the Blendie blender, which responds to vocal imitations, evoke playful and emotional responses, enhancing engagement.
Techniques for Sonic Innovation
Both frameworks underline the importance of crafting meaningful sonic interactions. Here’s how designers can apply these insights:
1. Leverage Auditory Feedback Loops: Use real-time feedback to enhance tasks requiring precision. A surgical tool that changes pitch based on pressure can guide users intuitively.
2. Foster Emotional Connections: Integrate sounds that mirror real-world actions or emotions. For example, soundscapes that reflect pouring water can make mundane interactions delightful.
3. Design for Multisensory Consistency: Ensure that sound complements visual and tactile feedback. Synchronizing auditory and visual cues can improve user understanding and create a seamless experience.
The Future of Interaction Design with Sound
As technology evolves, sound’s role in interaction design will expand—from aiding navigation in virtual reality to enhancing everyday products with subtle, meaningful audio cues. By combining cognitive insights with creative sound design, we can craft experiences that are not only functional but also profoundly human.
Reference
T. Hermann, A. Hunt, and J. G. Neuhoff, Eds., The Sonification Handbook, 1st ed. Berlin, Germany: Logos Publishing House, 2011, 586 pp., ISBN: 978-3-8325-2819-5.
DataSonifyer is a free online tool (no registration required) that turns data into sound. It creates “audible” information from numeric values by translating the datasets into musical parameters (pitch, volume, rhythm, etc.). The result is similar to a musical score that can be played and recorded. DataSonifyer was developed in 2023 by Christian Basl, supported by the Innovation Fund of the Science Press Conference.
TwoTone is a free, web-based tool (no downloads required) that turns data into sound and music—no coding or musical expertise necessary. Originally developed by Datavized Technologies with support from the Google News Initiative and now maintained by Sonify, the project was commissioned by Simon Rogers at Google and advised by Alberto Cairo. TwoTone uses data sonification to help users understand complex datasets and create data-driven compositions, offering an intuitive interface that works on desktops, tablets, and phones.
Music Algorithms offers a step-by-step approach to creating your own music from data—no advanced musical knowledge required. Simply load or paste a comma-separated sequence of numbers, then use a series of tools to map those values into musical pitches and durations, lock them to a scale, and finally play and export your composition as a MIDI file. Whether you’re exploring algorithmic composition or just experimenting with aural representations of data, these interactive features let you transform numbers into creative soundscapes.
Max is a flexible, visual programming environment originally developed by Miller Puckette at IRCAM in the 1980s. Though not specifically designed for data sonification, it offers that capability. While Max does have a steep learning curve, it also boasts extensive documentation, a wealth of tutorials, and a supportive user community that shares tips and instructional videos.
Pure Data
This free open source alternative to MAX is well documented by its community but might not be as beginner friendly.
