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SISO and SIMO Accompaniment Cancellation for Live Solo Recordings Based on Short-Time ERB-Band Wiener Filtering and Spectral Subtraction
Sound · Submitted 27 Nov 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our findings underpin that adaptive filtering is inapt of dealing with music signals and that Wiener filtering in the short-time Fourier transform domain is a much more effective approach.
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The abstract, sentence by sentence
Research in collaborative music learning is subject to unresolved problems demanding new technological solutions. One such problem poses the suppression of the accompaniment in a live recording of a performance during practice, which can be for the purposes of self-assessment or further machine-aided analysis. Being able to separate a solo from the accompaniment allows to create learning agents that may act as personal tutors and help the apprentice improve his or her technique. First, we start from the classical adaptive noise cancelling approach, and adjust it to the problem at hand. In a second step, we compare some adaptive and Wiener filtering approaches and assess their performances on the task. Our findings underpin that adaptive filtering is inapt of dealing with music signals and that Wiener filtering in the short-time Fourier transform domain is a much more effective approach. In addition, it is very cheap if carried out in the frequency bands of auditory filters. A double-output extension based on maximal-ratio combining is also proposed.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Research in collaborative music learning is subject to unresolved problems demanding new technological solutions. | background 0.87 | 0.04 | 0.01 | asserted 1.00 |
| s1 | One such problem poses the suppression of the accompaniment in a live recording of a performance during practice, which can be for the purposes of self-assessment or further machine-aided analysis. | background 0.95 | 0.05 | 0.02 | asserted 0.88 |
| s2 | Being able to separate a solo from the accompaniment allows to create learning agents that may act as personal tutors and help the apprentice improve his or her technique. | background 0.86 | 0.06 | 0.03 | hedged possibility 0.87 |
| s3 | First, we start from the classical adaptive noise cancelling approach, and adjust it to the problem at hand. | method 0.95 | 0.83 | 0.02 | asserted 0.80 |
| s4 | In a second step, we compare some adaptive and Wiener filtering approaches and assess their performances on the task. | method 0.91 | 0.92 | 0.04 | asserted 0.54 |
| s5 | Our findings underpin that adaptive filtering is inapt of dealing with music signals and that Wiener filtering in the short-time Fourier transform domain is a much more effective approach. | result 0.96 | 0.93 | 0.86 | asserted 1.00 |
| s6 | In addition, it is very cheap if carried out in the frequency bands of auditory filters. | result 0.72 | 0.64 | 0.07 | asserted 0.97 |
| s7 | A double-output extension based on maximal-ratio combining is also proposed. | contribution 0.76 | 0.86 | 0.05 | asserted 0.98 |
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The paper
Original abstract
Research in collaborative music learning is subject to unresolved problems demanding new technological solutions. One such problem poses the suppression of the accompaniment in a live recording of a performance during practice, which can be for the purposes of self-assessment or further machine-aided analysis. Being able to separate a solo from the accompaniment allows to create learning agents that may act as personal tutors and help the apprentice improve his or her technique. First, we start from the classical adaptive noise cancelling approach, and adjust it to the problem at hand. In a second step, we compare some adaptive and Wiener filtering approaches and assess their performances on the task. Our findings underpin that adaptive filtering is inapt of dealing with music signals and that Wiener filtering in the short-time Fourier transform domain is a much more effective approach. In addition, it is very cheap if carried out in the frequency bands of auditory filters. A double-output extension based on maximal-ratio combining is also proposed.
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