Music Dereverberation by Spectral Linear Prediction in Live Recordings

نویسندگان

  • Katariina Mahkonen
  • Antti Eronen
  • Tuomas Virtanen
  • Elina Helander
  • Victor Popa
  • Jussi Leppänen
  • Igor D.D. Curcio
چکیده

In this paper, we present our evaluations in using blind single channel dereverberation on music signals. The target material is heavily reverberated and dynamic range compressed polyphonic music from several genres. The applied dereverberation method is based on spectral subtraction regulated by a time-frequency domain linear predictive model. We present our results on enhancing music signal quality and automatic beat tracking accuracy with the proposed dereverberation method. Signal quality enhancement, measured by improvement in signal to distortion ratio, is achieved for both reverberant and dynamic range compressed signals. Moreover, the algorithm shows potential as a preprocessing method for music beat tracking.

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Title Placeholder

A speech signal captured by a distant microphone is generally contaminated by reverberation and background noise, which severely degrade the automatic speech recognition (ASR) performance. In this paper, we first extend a previously proposed single channel dereverberation algorithm to a multi-channel scenario. The method estimates late reflections using multichannel multi-step linear prediction...

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تاریخ انتشار 2013