Chest Falsetto

--- license: mit datasets: - ccmusic-database/chest_falsetto language: - en metrics: - accuracy pipeline_tag: audio-classification tags: - music - art --- # Intro The design of the chest-falsetto voice discrimination model aims to effectively differentiate between real and synthetic voices in audio samples, with four specific categories including male chest, male falsetto, female chest, and female falsetto voices. The model's training is based on a backbone network from the computer vision (CV) domain, which involves transforming audio data into spectrograms and fine-tuning to enhance the network's accuracy in recognizing different voice categories. During training, a dataset containing both real and synthetic voice samples is utilized to ensure the model adequately learns and captures features relevant to male and female chest and falsetto voices. Through this approach, the model can finely classify different genders and chest/falsetto voices, providing a reliable solution for accurate voice discrimination in audio. This model holds broad potential applications in fields such as speech processing and music production, offering an efficient and precise tool for audio analysis and processing. Its training and fine-tuning strategies based on computer vision principles highlight the model's adaptability and robustness across different domains, providing beneficial examples for further research and application. ## Demo (inference code) <https://huggingface.co/spaces/ccmusic-database/chest_falsetto> ## Usage ```python from huggingface_hub import snapshot_download model_dir = snapshot_download( "ccmusic-database/chest_falsetto", cache_dir="./__pycache__", ) print(model_dir) ``` ## Maintenance ```bash GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:ccmusic-database/chest_falsetto cd chest_falsetto ``` ## Results | Backbone | Mel | CQT | Chroma | | :---------------: | :-------------------------: | :---------: | :---------: | | Swin-S V2 | **_0.968_** | 0.268 | **_0.268_** | | MaxViT-T | 0.820 | **_0.933_** | 0.250 | | | | | | | AlexNet | [**_0.994_**](#best-result) | **_0.963_** | **_0.586_** | | ShuffleNet V2 2.0 | 0.939 | 0.669 | 0.222 | | GoogleNet | 0.983 | 0.274 | 0.292 | | MNASNet-A3 | 0.756 | 0.260 | 0.320 | | SqueezeNet 1.1 | 0.963 | 0.900 | 0.378 | | Average | 0.918 | 0.610 | 0.331 | ### Best Result <style> #falsetto td { vertical-align: middle !imp...

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