Mixed stereo audio classification using a stereo-input mixed-to-panned level feature

Research output: Contribution to journalArticle

Abstract

Many past studies have been conducted on speech/music discrimination due to the potential applications for broadcast and other media; however, it remains possible to expand the experimental scope to include samples of speech with varying amounts of background music. This paper focuses on the development and evaluation of two measures of the ratio between speech energy and music energy: a reference measure called speech-to-music ratio (SMR), which is known objectively only prior to mixing, and a feature called the stereo-input mix-to-peripheral level feature (SIMPL), which is computed from the stereo mixed signal as an imprecise estimate of SMR. SIMPL is an objective signal measure calculated by taking advantage of broadcast mixing techniques in which vocals are typically placed at stereo center, unlike most instruments. Conversely, SMR is a hidden variable defined by the relationship between the powers of portions of audio attributed to speech and music. It is shown that SIMPL is predictive of SMR and can be combined with state-of-the-art features in order to improve performance. For evaluation, this new metric is applied in speech/music (binary) classification, speech/music/mixed (trinary) classification, and a new speech-to-music ratio estimation problem. Promising results are achieved, including 93.06% accuracy for trinary classification and 3.86 dB RMSE for estimation of the SMR.

Original languageEnglish (US)
Pages (from-to)2025-2033
Number of pages9
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume22
Issue number12
DOIs
StatePublished - Dec 1 2014

Keywords

  • Audio classification
  • Audio processing
  • Audio segmentation
  • Classification algorithms
  • Gaussian mixturemodel
  • Melfrequency cepstral coefficients
  • Music information retrieval
  • Music processing
  • Speech processing
  • Speech/music discrimination

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Computational Mathematics
  • Electrical and Electronic Engineering

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