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“…The existing algorithm from Fuhrmann and Herrera [45] used typical hand-made timbral audio features with their framewise mean and variance statistics to train SVMs, and Bosch et al [44] improved this algorithm with source separation called FASST (Flexible Audio Source Separation Framework) [46] in a preprocessing step. In terms of precision, Fuhrmann and Herrera's algorithm showed the best performance for both the micro and the macro measure.…”
Section: A Comparison To Existing Algorithmsmentioning
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“…The existing algorithm from Fuhrmann and Herrera [45] used typical hand-made timbral audio features with their framewise mean and variance statistics to train SVMs, and Bosch et al [44] improved this algorithm with source separation called FASST (Flexible Audio Source Separation Framework) [46] in a preprocessing step. In terms of precision, Fuhrmann and Herrera's algorithm showed the best performance for both the micro and the macro measure.…”
Section: A Comparison To Existing Algorithmsmentioning
“…Joint spatial and spectral modeling [5,6] and convolutive NMF have contributed to the reduction of the keyword error rate for small-vocabulary automatic speech recognition (ASR) from 44% down to as little as 8% in a strongly guided real-world domestic scenario involving knowledge of the speaker and his/her spatial position [32]. Finally, weakly guided separation of percussive and harmonic content in music has helped several music information retrieval (MIR) tasks, reducing for instance the relative error rate for chord recognition by 28% [33].…”
Section: Impact and Perspectivesmentioning
“…Before describing two-stage HPSS, let us first discuss HPSS [34], [35], [36], [50] briefly. HPSS is a technique that separates a spectrogram Y into two components below,…”
Section: Harmonic/percussive Sound Separation (Hpss)mentioning