Messymashupclassifier
This model was developed to identify musical genres in complex, noisy audio environments. It was built as part of a deep learning project focused on the Messy Mashup challenge, where the goal was to classify genres...
Source du modèle
- Type
- Other
- Taille du fichier
- 329 MB
- Modèle de base
- MIT/ast-finetuned-audioset-10-10-0.4593
- Version
- vRevision 24755f4
- Mis à jour
- 12 sept. 2026
- Téléchargements
- 4
- Source
- Hugging Face
Description de la source
Audio Spectrogram Transformer (AST) for Music Genre Classification
This model was developed to identify musical genres in complex, noisy audio environments. It was built as part of a deep learning project focused on the Messy Mashup challenge, where the goal was to classify genres even when multiple instrument tracks and environmental noises overlap.
Model Overview
This model is based on the Audio Spectrogram Transformer (AST). Unlike traditional models that look at audio as a simple sequence of sounds, the AST treats an audio spectrogram like a high-resolution image. It uses an Attention Mechanism to look at the entire 10-second clip at once, allowing it to pick up on both subtle instrument textures and overall rhythmic patterns.
Technical Details
- Architecture: Audio Spectrogram Transformer (AST)
- Sampling Rate: 16,000 Hz
- Input Length: 10 Seconds
- Feature Type: Log-Mel Spectrogram (128 Mel bands)
- Performance: Achieved a 0.85 Macro F1-score on the evaluation set.
The Training Strategy: Cross-Song Recombination
The main reason this model performs well is a training technique called Cross-Song Recombination.
In most training setups, a model listens to a single song at a time. To make this model more resilient to noise and "messy" audio, we created synthetic mashups during training. We took individual "stems" (the separate recordings for bass, drums, vocals, and other instruments) from different songs within the same genre and mixed them together.
We also added environmental noise from the ESC-50 dataset. This forced the model to ignore the chaos and focus only on the core spectral patterns that define a genre—such as the specific frequency of a blues guitar or the tempo of a techno beat.
Supported Genres
The model is trained to classify audio into one of the following 10 categories:
- Blues
- Classical
- Country
- Disco
- Hiphop
- Jazz
- Metal
- Pop
- Reggae
- Rock
How to Use
Visit my huggingfaces spaces where you can check what genre your .wav file is!
Sources
1 source
Sources
1 sourceVérifié 29 août
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
2 extraits
Extraits de sources
2 extraits--- license: mit language: - en metrics: - f1 - accuracy base_model: - MIT/ast-finetuned-audioset-10-10-0.4593 pipeline_tag: audio-classification tags: - music - audio-transformers --- # Audio Spectrogram Transformer (AST) for Music Genre Classification This model was developed to identify musical genres in complex, noisy audio environments. It was built as part of a deep learning project focused on the **Messy Mashup** challenge, where the goal was to classify genres even when multiple instrument tracks and environmental noises overlap. --- ## Model Overview This model is based on the **Audio Spectrogram Transformer (AST)**. Unlike traditional models that look at audio as a simple sequence of sounds, the AST treats an audio spectrogram like a high-resolution image. It uses an **Attention Mechanism** to look at the entire 10-second clip at once, allowing it to pick up on both subtle instrument textures and overall rhythmic patterns. ### Technical Details * **Architecture:** Audio Spectrogram Transformer (AST) * **Sampling Rate:** 16,000 Hz * **Input Length:** 10 Seconds * **Feature Type:** Log-Mel Spectrogram (128 Mel bands) * **Performance:** Achieved a **0.85 Macro F1-score** on the evaluation set. --- ## The Training Strategy: Cross-Song Recombination The main reason this model performs well is a training technique called **Cross-Song Recombination**. In most training setups, a model listens to a single song at a time. To make this model more resilient to noise and "messy" audio, we created synthetic mashups during training. We took individual "stems" (the separate recordings for bass, drums, vocals, and other instruments) from different songs within the same genre and mixed them together. We also added environmental noise from the ESC-50 dataset. This forced the model to ignore the chaos and focus only on the core spectral patterns that define a genre—such as the specific frequency of a blues guitar or the tempo of a techno beat. --- ## Supported Genres The model is trained to classify audio into one of the following 10 categories: 1. **Blues** 2. **Classical** 3. **Country** 4. **Disco** 5. **Hiphop** 6. **Jazz** 7. **Metal** 8. **Pop** 9. **Reggae** 10. **Rock** --- ## How to Use Visit my huggingfaces spaces where you can check what genre your .wav file is! [click here!](https://huggingface.co/spaces/afloven/messymashupclassificati...
Source context: 4 downloads · 1 likes · Pipeline audio-classification · Repo afloven/messymashupclassifier
