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 work is three-fold: First, we present a new method for VD that passes a compact set of features to an LSTM-RNN 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 data sets available to the research community. Third, 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 utilising loudness related features and show that our method successfully deals with this problem due to the specific set of features it uses.
Sprache der Kurzfassung:
IEEE Transactions on Audio, Speech and Language Processing