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Evaluation of Audio Classification Models
Himani
Introduction
Audio classification is a crucial field in machine learning that enables systems to recognize and categorize different types of audio signals. It has widespread applications, including speech recognition, emotion detection, language identification, and sound event detection. In this blog, we evaluate six different audio classification models to understand their capabilities and performance in various domains [1].
Why is Audio Classification Important?
Audio classification plays a significant role in several real-world applications:
- Gender Classification: Used in voice assistants, customer support, and accessibility tools [2].
- Emotion Classification: Helps in mental health monitoring, human-computer interaction, and customer sentiment analysis [3].
- Language Detection: Essential for multilingual applications, automated transcription services, and content moderation [4].
- Sound Detection: Used in security surveillance, wildlife monitoring, and smart home automation [5].
Evaluated Models
We have selected six models for evaluation, each excelling in a specific aspect of audio classification:
| Model | Feature | Architecture | Use Cases |
|---|---|---|---|
| Audeering/wav2vec2 | Gender Classification | Wav2Vec2 | Voice Assistants, Customer Support [1] |
| FunASR/emotion2vec | Emotion Recognition | Custom Deep Learning | Sentiment Analysis, Human-Computer Interaction [3] |
| Superb/wav2vec2 | Emotion Recognition | Wav2Vec2 | Sentiment Analysis, Human-Computer Interaction [2] |
| MIT/ast-finetuned-audioset | Sound Detection | Audio Spectrogram Transformer (AST) | Smart Home Automation [4] |
| Facebook-MMS | Language Identification | Transformer | Voice Assistants, Automatic Transcription [5] |
| OpenAI-Whisper | Language Identification | Transformer | Voice Assistants, Automatic Transcription [6] |
Comparative Analysis
| Model | Processing Time (s) | Accuracy | Model Size (MB) | License |
|---|---|---|---|---|
| Audeering/wav2vec2 | 79.3144 | High | 120 | Apache 2.0 |
| FunASR/emotion2vec | 20.0636 | Medium | 85 | MIT |
| Superb/wav2vec2 | 5.0744 | High | 98 | Apache 2.0 |
| MIT/ast-finetuned-audioset | 13.2893 | Medium | 150 | Apache 2.0 |
| Facebook-MMS | 119.6986 | High | 200 | CC-BY-SA |
| OpenAI-Whisper | 102.6909 | Very High | 155 | OpenAI |
Check Out Our Results
Audeering/wav2vec2
Input Audio
Output - Predicted Gender: Male
FunASR/emotion2vec and Superb/wav2vec2
Input Audio
Output - FunASR - angry
Output - Superb - ang
MIT/ast-finetuned-audioset
Input Audio
Output - Animal
Facebook-MMS and OpenAI-Whisper
Input Audio
Output - Facebook/MMS - hin
Output - OpenAI/Whisper - hi
Conclusion
Audio classification models have diverse applications and are evolving rapidly with advancements in deep learning. By understanding the differences in architecture, training data, and use cases, developers can choose the most suitable model for their specific application [7]. Future improvements in multimodal learning and larger datasets will continue to push the boundaries of audio classification performance.
References
- Audeering/wav2vec2 - https://huggingface.co/audeering/wav2vec2-large-robust-24-ft-age-gender
- Superb/wav2vec2 - https://huggingface.co/superb/wav2vec2-base-superb-er
- FunASR/emotion2vec - https://huggingface.co/emotion2vec/emotion2vec_plus_large
- MIT/ast-finetuned-audioset - https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593
- Facebook-MMS - https://huggingface.co/facebook/mms-lid-256
- OpenAI-Whisper - https://huggingface.co/openai/whisper-small
- Google AudioSet - https://research.google.com/audioset/
Open-Source Contribution
For detailed information, implementation guides, and benchmarking scripts, visit our public GitHub repository:
GitHub Repository: Evaluation of Audio Classification Models on GitHub
Our public repository allows contributions from the community. Feel free to:
- Fork the repository
- Submit pull requests for improvements or bug fixes
- Report issues and suggest enhancements