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AUDIO BASED DISAMBIGUATION OF MUSIC GENRE TAGS Romain Mannequin, Jimena RoyoLetelier, Manuel Moussaka Deezer R&D, Paris research@deezer.comABSTRACT In this paper, we propose to infer music genre embeddings
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How to fill out audio-based metric learning for
How to fill out audio-based metric learning for
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To fill out audio-based metric learning, follow these steps:
02
Collect a dataset of audio recordings that you will use for training the metric learning model.
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Preprocess the audio data by extracting audio features such as MFCC or spectrograms.
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Split the dataset into training and validation sets.
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Design or choose a suitable metric learning algorithm, such as triplet loss or contrastive loss.
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Train the metric learning model using the training dataset.
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Evaluate the model performance using the validation dataset and optimize hyperparameters if necessary.
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Once the model is trained and evaluated, it can be used for audio-based similarity or classification tasks.
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Provide new audio samples to the model and obtain embeddings or similarity scores based on the learned metric.
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Use the obtained embeddings or similarity scores for downstream applications such as speaker identification, music genre classification, or audio retrieval.
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