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Learning musical instruments from mixtures of audio with weak labels
- In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR
, 2008
"... We are interested in developing a system that learns to recognize individual sound sources in an auditory scene where multiple sources may be occurring simultaneously. We focus here on sound source recognition in music audio mixtures. Many researchers have made progress by using isolated training ex ..."
Abstract
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Cited by 4 (0 self)
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We are interested in developing a system that learns to recognize individual sound sources in an auditory scene where multiple sources may be occurring simultaneously. We focus here on sound source recognition in music audio mixtures. Many researchers have made progress by using isolated training examples or very strongly labeled training data. We consider an alternative approach: the learner is presented with a variety of weaky-labeled mixtures. Positive examples include the target instrument at some point in a mixture of sounds, and negative examples are mixtures that do not contain the target. We show that it not only possible to learn from weakly-labeled mixtures of instruments, but that it works significantly better (78 % correct labeling compared to 55%) than learning from isolated examples when the task is identification of an instrument in novel mixtures. 1
ISMIR 2008 – Session 1c – Timbre LEARNING MUSICAL INSTRUMENTS FROM MIXTURES OF AUDIO WITH WEAK LABELS
"... We are interested in developing a system that learns to recognize individual sound sources in an auditory scene where multiple sources may be occurring simultaneously. We focus here on sound source recognition in music audio mixtures. Many researchers have made progress by using isolated training ex ..."
Abstract
- Add to MetaCart
We are interested in developing a system that learns to recognize individual sound sources in an auditory scene where multiple sources may be occurring simultaneously. We focus here on sound source recognition in music audio mixtures. Many researchers have made progress by using isolated training examples or very strongly labeled training data. We consider an alternative approach: the learner is presented with a variety of weaky-labeled mixtures. Positive examples include the target instrument at some point in a mixture of sounds, and negative examples are mixtures that do not contain the target. We show that it not only possible to learn from weakly-labeled mixtures of instruments, but that it works significantly better (78 % correct labeling compared to 55%) than learning from isolated examples when the task is identification of an instrument in novel mixtures. 1
