Singing Voice Recognition for Music Information Retrieval

This thesis proposes signal processing methods for analysis of singing voice audio signals, with the objectives of obtaining information about the identity and lyrics content of the singing. Two main topics are presented, singer identification in monophonic and polyphonic music, and lyrics transcription and alignment. The information automatically extracted from the singing voice is meant to be used for applications such as music classification, sorting and organizing music databases, music information retrieval, etc. For singer identification, the thesis introduces methods from general audio classification and specific methods for dealing with the presence of accompaniment. The emphasis is on singer identification in polyphonic audio, where the singing voice is present along with musical accompaniment. The presence of instruments is detrimental to voice identification performance, and eliminating the effect of instrumental accompaniment is an important aspect of the problem. The study of singer identification is centered around the degradation of classification performance in presence of instruments, and separation of the vocal line for improving performance. For the study, monophonic singing was mixed with instrumental accompaniment at different signal-to-noise (singing-to-accompaniment) ratios and the classification process was performed on the polyphonic mixture and on the vocal line separated from the polyphonic mixture. The method for classification including the step for separating the vocals is improving significantly the performance compared to classification of the polyphonic mixtures, but not close to the performance in classifying the monophonic singing itself. Nevertheless, the results show that classification of singing voices can be done robustly in polyphonic music when using source separation. In the problem of lyrics transcription, the thesis introduces the general speech recognition framework and various adjustments that can be done before applying the methods on singing voice. The variability of phonation in singing poses a significant challenge to the speech recognition approach. The thesis proposes using phoneme models trained on speech data and adapted to singing voice characteristics for the recognition of phonemes and words from a singing voice signal. Language models and adaptation techniques are an important aspect of the recognition process. There are two different ways of recognizing the phonemes in the audio: one is alignment, when the true transcription is known and the phonemes have to be located, other one is recognition, when both transcription and location of phonemes have to be found. The alignment is, obviously, a simplified form of the recognition task. Alignment of textual lyrics to music audio is performed by aligning the phonetic transcription of the lyrics with the vocal line separated from the polyphonic mixture, using a collection of commercial songs. The word recognition is tested for transcription of lyrics from monophonic singing. The performance of the proposed system for automatic alignment of lyrics and audio is sufficient for facilitating applications such as automatic karaoke annotation or song browsing. The word recognition accuracy of the lyrics transcription from singing is quite low, but it is shown to be useful in a query-by-singing application, for performing a textual search based on the words recognized from the query. When some key words in the query are recognized, the song can be reliably identified

Statistical Approaches for Signal Processing with Application to Automatic Singer Identification

In the music world, the oldest instrument is known as the singing voice that plays an important role in musical recordings. The singer\u27s identity serves as a primary aid for people to organize, browse, and retrieve music recordings. In this thesis, we focus on the problem of singer identification based on the acoustic features of singing voice. An automatic singer identification system is constructed and has achieved a very high identification accuracy. This system consists of three crucial parts: singing voice detection, background music removal and pattern recognition. These parts are introduced and explored in great details in this thesis. To be specific, in terms of the singing voice detection, we firstly study a traditional method, double GMM. Then an improved method, namely single GMM, is proposed. The experimental result shows that the detection accuracy of single GMM can be achieved as high as 96.42%. In terms of the background music removal, Non-negative Matrix Factorization (NMF) and Robust Principal Component Analysis (RPCA) are demonstrated. The evaluation result shows that RPCA outperforms NMF. In terms of pattern recognition, we explore the algorithms of Support Vector Machine (SVM) and Gaussian Mixture Model (GMM). Based on the experimental results, it turns out that the prediction accuracy of GMM classifier is about 16% higher than SVM

Vocal piano accompaniment: A constant research towards emancipation (2)

This is the second and last article in the series dedicated to the investigation of the evolution of vocal piano accompaniment through history and the role-played by the piano in its relationship with the voice. If the previous chapter focused from the beginnings of piano accompaniment to Franz Schubert, this one will take up again the analysis of the piano part from the last lieder of Franz Schubert to Arnold Schoenberg. The research method used continues to be based on the musical analysis of the piano part, addressing both issues of the piano itself and its link with the text and the vocal part. By means of musical examples of different composers, the article investigates all those aspects that provide evidence of the change in the role of the piano in the correspondence of the piano with the voice and its repercussion on the final result of the work. In this way, we will look at the history of piano accompaniment for voice and see how the influence of Schubert's conception of vocal piano accompaniment materializes in contemporary and later composers, as well as the crucial change in the history of the piano as an accompanying instrument introduced by Schubert. The results and conclusions drawn from the evolution presented in these two articles are presented at the end of the article, based on the different aspects involved in the musical event, such as rhythm, harmony or texture

The songwriting coalface: where multiple intelligences collide

This paper investigates pedagogy around songwriting professional practice. Particular focus is given to the multiple intelligence theory of Howard Gardner as a lens through which to view songwriting practice, referenced to recent songwriting‐specific research (e.g. McIntyre, Bennett). Songwriting education provides some unique challenges; firstly, due to the qualitative nature of assessment and the complex and multi‐faceted nature of skills necessary (lyric writing, composing, recording, and performing), and secondly, in some less‐tangible capacities beneficial to the songwriter (creative skills, and nuanced choice‐making). From the perspective of songwriting education, Gardner’s MI theory provides a ‘useful fiction’ (his term) for knowledge transfer in the domain, especially (and for this researcher, surprisingly) in naturalistic intelligence

The Art of Practice – Understanding the process of musical maturation through reflection.

Much has been written in the last 30 years about musical practice and performance, but there is little consensus over what practice really means, or how musicians progress by practising. Researchers tend to focus on specific elements in practice rather than maintaining a more holistic perspective. Whilst academics historically focused on (primarily Western) classical musicians, more recent research has encompassed popular, jazz and folk musicians. The current research project at the University of Liverpool focuses on the practice and performance of both popular and classical musicians as described in students’ reflective essays. We posit a model for musical maturation that incorporates key elements from psychology, epistemology and sociocultural theory. (DIPF/Orig.