Lyrics recognition from a singing voice is achieved by similar technology to that of speech recognition. In order to develop a MIR system that uses lyrics and melody infor- mation, lyrics recognition is needed.

Although the lyrics information is useful for retrieval, there have been few at- tempts to exploit lyrics in the user's input. All of these systems use only the melody information for retrieval. Recently, several music information retrieval (MIR) sys- tems have been developed which retrieve musical pieces by the user's singing voice. We show that the recognition of these diffcult chords in particular is substantially improved by the prior approximate transcription using NNLS. The nature of some chords makes their identification particularly susceptible to confusion between fundamental frequency and partials. This is a significant increase over the top result (74%) in MIREX 2009. We achieve very good results of 80% accuracy using the song collection and metric of the 2009 MIREX Chord Detection tasks. The resulting NNLS chroma features are tested by using them as an input to an existing state-of-the-art high-level model for chord transcription. We do so by performing a prior approximate transcription using an existing technique to solve non-negative least squares problems (NNLS). In this paper we reverse this approach and seek to find chroma features that are more suitable for usage in a musically-motivated model. Research on the front end of chord transcription algorithms has often concentrated on finding good chord templates to fit the chroma features.

The choice of chord profiles and higher-level time-series modelling have received a lot of attention, resulting in methods with an overall performance of more than 70% in the MIREX Chord Detection task 2009. The automatic detection and transcription of musical chords from audio is an established music computing task.

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