Online, Loudness-Invariant Vocal Detection in Mixed Music Signals

Singing voice detection, also referred to as vocal detection (VD), aims at automatically identifying the regions in a music recording where at least one person sings. It is highly challenging due to the timbral and expressive richness of the human singing voice, as well as the practically endless va...

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Bibliographic Details
Published in:IEEE/ACM transactions on audio, speech, and language processing speech, and language processing, 2018-08, Vol.26 (8), p.1369-1380
Main Authors: Lehner, Bernhard, Schluter, Jan, Widmer, Gerhard
Format: Article
Language:English
Subjects:
Feature extraction
Hidden Markov models
Instruments
LSTM-RNN
music information retrieval
neural network
Singing voice detection
Spectrogram
Speech
Speech processing
Task analysis
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Summary:Singing voice detection, also referred to as vocal detection (VD), aims at automatically identifying the regions in a music recording where at least one person sings. It is highly challenging due to the timbral and expressive richness of the human singing voice, as well as the practically endless variety of interfering instrumental accompaniment. Additionally, certain instruments have an inherent risk of being misclassified as vocals due to similarities of the sound production system. In this paper, we present a machine learning approach that is based on our previous work for VD, which is specifically designed to deal with those challenging conditions. The contribution of this paper is threefold: First, we present a new method for VD that passes a compact set of features to a long short-term memory recurrent neural network classifier that obtains state-of-the-art results. Second, we thoroughly evaluate the proposed method along with related approaches to really probe the weaknesses of the methods. In order to allow for such a thorough evaluation, we make a curated collection of datasets available to the research community. Finally, we focus on a specific problem that was not obvious and had not been discussed in the literature so far. The reason for this is precisely because limited evaluations had not revealed this as a problem: the lack of loudness invariance. We will discuss the implications of utilizing loudness-related features and show that our method successfully deals with this problem due to the specific set of features it uses.
ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2018.2825108