This paper presents a new method of singing voice analysis that performs mutually-dependent singing voice separation and vocal fundamental frequency F0 estimation. Vocal F0 estimation is considered to become easier if singing voices can be separated from a music audio signal, and vocal F0 contours are useful for singing voice separation. This calls for an approach that improves the performance of each of these tasks by using the results of the other. The proposed method first performs robust principal component analysis RPCA for roughly extracting singing voices from a target music audio signal. The F0 contour of the main melody is then estimated from the separated singing voices by finding the optimal temporal path over an F0 saliency spectrogram. Finally, the singing voices are separated again more accurately by combining a conventional time-frequency mask given by RPCA with another mask that passes only the harmonic structures of the estimated F0s. Experimental results showed that the proposed method significantly improved the performances of both singing voice separation and vocal F0 estimation. The proposed method also outperformed all the other methods of singing voice separation submitted to an international music analysis competition called MIREX 2014.
A typical approach to vocal F0 estimation is to identify F0s that have predominant harmonic structures by using an F0 saliency spectrogram that represents how likely the F0 is to exist in each time-frequency bin. A core of this approach is how to estimate a saliency spectrogram @cite_39 @cite_15 @cite_18 @cite_23 @cite_43 . Goto @cite_39 proposed a statistical multiple-F0 analyzer called PreFEst that approximates an observed spectrum as a superimposition of harmonic structures. Each harmonic structure is represented as a Gaussian mixture model (GMM) and the mixing weights of GMMs corresponding to different F0s can be regarded as a saliency spectrum. Rao @cite_15 tracked multiple candidates of vocal F0s including the F0s of locally predominant non-vocal sounds and then identified vocal F0s by focusing on the temporal instability of vocal components. Dressler @cite_18 attempted to reduce the number of possible overtones by identifying which overtones are derived from a vocal harmonic structure. Salamon @cite_43 proposed a heuristics-based method called MELODIA that focuses on the characteristics of vocal F0 contours. The contours of F0 candidates are obtained by using a saliency spectrogram based on subharmonic summation. This method achieved the state-of-the-art results in vocal F0 estimation.
@cite_18 @cite_39 @cite_43 @cite_23 @cite_15
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Abstract In this paper, we describe the concept of music scene description and address the problem of detecting melody and bass lines in real-world audio signals containing the sounds of various instruments. Most previous pitch-estimation methods have had difficulty dealing with such complex music signals because these methods were designed to deal with mixtures of only a few sounds. To enable estimation of the fundamental frequency (F0) of the melody and bass lines, we propose a predominant-F0 estimation method called PreFEst that does not rely on the unreliable fundamental component and obtains the most predominant F0 supported by harmonics within an intentionally limited frequency range. This method estimates the relative dominance of every possible F0 (represented as a probability density function of the F0) by using MAP (maximum a posteriori probability) estimation and considers the F0’s temporal continuity by using a multiple-agent architecture. Experimental results with a set of ten music excerpts from compact-disc recordings showed that a real-time system implementing this method was able to detect melody and bass lines about 80 of the time these existed. A new method for the estimation of multiple concurrent pitches in piano recordings is presented. It addresses the issue of overlapping overtones by modeling the spectral envelope of the overtones of each note with a smooth autoregressive model. For the background noise, a moving-average model is used and the combination of both tends to eliminate harmonic and sub-harmonic erroneous pitch estimations. This leads to a complete generative spectral model for simultaneous piano notes, which also explicitly includes the typical deviation from exact harmonicity in a piano overtone series. The pitch set which maximizes an approximate likelihood is selected from among a restricted number of possible pitch combinations as the one. Tests have been conducted on a large homemade database called MAPS, composed of piano recordings from a real upright piano and from high-quality samples. Abstract The present paper describes a corpus-based singing voice syn-thesis system based on hidden Markov models (HMMs). Thissystem employs the HMM-based speech synthesis to synthesizesingingvoice. Musical information such aslyrics, tones, durationsis modeled simultaneously in a uniﬁed framework of the context-dependent HMM. It can mimic the voice quality and singing styleof the original singer. Results of a singing voice synthesis exper-iment show that the proposed system can synthesize smooth andnatural-sounding singing voice. Index Terms : singing voice synthesis, HMM, time-lag model. 1. Introduction In recent years, various applications of speech synthesis systemshave been proposed and investigated. Singing voice synthesis isone of the hot topics in this area [1–5]. However, only a fewcorpus-based singing voice synthesis systems which can be con-structed automatically have been proposed.Currently, there are two main paradigms in the corpus-basedspeech synthesis area: sample-based approach and statistical ap-proach. The sample-based approach such as unit selection [6]can synthesize high-quality speech. However, it requires a hugeamountoftrainingdatatorealizevariousvoicecharacteristics. Onthe other hand, the quality of statistical approach such as HMM-basedspeechsynthesis[7]isbuzzybecauseitisbasedonavocod-ingtechnique. However,itissmoothandstable,anditsvoicechar-acteristics can easily be modiﬁed by transforming HMM parame-ters appropriately. For singing voice synthesis, applying the unitselection seems to be difﬁcult because a huge amount of singingspeech which covers vast combinations of contextual factors thataffect singing voice has to be recorded. On the other hand, theHMM-based system can be constructed using a relatively smallamount of training data. From this point of view, the HMM-basedapproach seems to be more suitable for the singing voice synthe-sizer. In the present paper, we apply the HMM-based synthesisapproach to singing voice synthesis.Although the singing voice synthesis system proposed in thepresent paper is quite similar to the HMM-based text-to-speechsynthesissystem[7],therearetwomaindifferencesbetweenthem.In the HMM-based text-to-speech synthesis system, contextualfactors which may affect reading speech (e.g. phonemes, sylla-bles, words, phrases, etc.) are taken into account. However, con-textual factors which may affect singing voice should be different Melody extraction algorithms for single-channel polyphonic music typically rely on the salience of the lead melodic instrument, considered here to be the singing voice. However the simultaneous presence of one or more pitched instruments in the polyphony can cause such a predominant-F0 tracker to switch between tracking the pitch of the voice and that of an instrument of comparable strength, resulting in reduced voice-pitch detection accuracy. We propose a system that, in addition to biasing the salience measure in favor of singing voice characteristics, acknowledges that the voice may not dominate the polyphony at all instants and therefore tracks an additional pitch to better deal with the potential presence of locally dominant pitched accompaniment. A feature based on the temporal instability of voice harmonics is used to finally identify the voice pitch. The proposed system is evaluated on test data that is representative of polyphonic music with strong pitched accompaniment. Results show that the proposed system is indeed able to recover melodic information lost to its single-pitch tracking counterpart, and also outperforms another state-of-the-art melody extraction system designed for polyphonic music.