Machine Learning: An Algorithmic Perspective
Machine Learning: An Algorithmic Perspective
Multipitch Analysis of Polyphonic Music and Speech Signals Using an Auditory Model
IEEE Transactions on Audio, Speech, and Language Processing
An experimental comparison of audio tempo induction algorithms
IEEE Transactions on Audio, Speech, and Language Processing
Separation of synchronous pitched notes by spectral filtering of harmonics
IEEE Transactions on Audio, Speech, and Language Processing
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In this paper, we propose a novel approach for music similarity estimation. It combines temporal segmentation of music signals with source separation into so-called tone objects. We solely use the timbre-related audio features Mel-Frequency Cepstral Coefficients (MFCC) and Octave-based Spectral Contrast (OSC) to describe the extracted tone objects. First, we compare our approach to a baseline system that employs frame-wise feature extraction and bag-of-frames classification. Second, we set up a system that extracts features on perfectly isolated single track recordings, achieving near perfect classification. Finally, we compare our novel approach against the basis experiments. We find that it clearly outperforms the baseline system in a five-class genre classification task. Our results indicate that tone object based feature extraction clearly improves music similarity estimation.