Automatic note-level transcription is considered one of the most challenging tasks in music information retrieval. The specific case of flamenco singing transcription poses a particular challenge due to its complex melodic progressions, intonation inaccuracies, the use of a high degree of ornamentation, and the presence of guitar accompaniment. In this study, we explore the limitations of existing state of the art transcription systems for the case of flamenco singing and propose a specific solution for this genre: We first extract the predominant melody and apply a novel contour filtering process to eliminate segments of the pitch contour which originate from the guitar accompaniment. We formulate a set of onset detection functions based on volume and pitch characteristics to segment the resulting vocal pitch contour into discrete note events. A quantised pitch label is assigned to each note event by combining global pitch class probabilities with local pitch contour statistics. The proposed system outperforms state of the art singing transcription systems with respect to voicing accuracy, onset detection, and overall performance when evaluated on flamenco singing datasets.
Nevertheless, the authors report mistakenly transcribed guitar notes as a main source of error and the inaccurate vocal detection a the major limitation of the system performance. The reported note transcription accuracies reported in @cite_2 with a note f-measure of slightly below @math furthermore indicate the difficulty of transcribing flamenco singing. Significantly higher performance is achieved when evaluating the same transcription algorithm on a dataset containing pop- and jazz singing excerpts. We therefore identify a need for improving the state of the art on flamenco singing transcription and design an algorithm robust towards the particular characteristics of the genre which is furthermore suitable for the analysis of accompanied flamenco singing recordings.
@cite_2
1990908299
This article deals with automatic transcription of flamenco music recordings-more specifically, a cappella singing. We first study the specifics of flamenco singing and propose a transcription system based on fundamental frequency and energy estimation, which incorporates an iterative strategy for note segmentation and labeling. The proposed approach is evaluated on a music collection of 72 performances, including a variety of singers and recording conditions, and the presence or absence of percussion, background voices, and noise. We obtain satisfying results for the different approaches tested, and our system outperforms a state-of-the-art approach designed for other singing styles. In this study, we discuss the difficulties found in transcribing flamenco singing and in evaluating the obtained transcriptions, we analyze the influence of the different steps of the algorithm, and we state the main limitations of our approach and discuss challenges for future studies. This paper proposes a method for the automatic transcription of singing melodies in polyphonic music. The method is based on multiple-F0 estimation followed by acoustic and musicological modeling. The acoustic model consists of separate models for singing notes and for no-melody segments. The musicological model uses key estimation and note bigrams to determine the transition probabilities between notes. Viterbi decoding produces a sequence of notes and rests as a transcription of the singing melody. The performance of the method is evaluated using the RWC popular music database for which the recall rate was 63 and precision rate 46 . A significant improvement was achieved compared to a baseline method fromMIREX05 evaluations. Abstract We present a system for the automatic transcription of solo human singing into note sequences. Particular attention is devoted to the modelling of the transitions between consecutive notes of the sung passage. Hidden Markov models (HMMs) are used to represent both individual notes and the transitions between them, in order to capture the variability of the estimated pitch within a statistical framework. A database consisting of annotated passages sung by 26 soprano subjects is compiled and used for the development of the system. Explicit transition models are introduced in order to better identify note boundaries that are otherwise poorly modelled. Context-independent transition models are evaluated first, followed by context-dependent transition models which are shown to improve both the note recognition accuracy and the transition region time-alignment. The final system is found to be able to transcribe sung passages with an overall accuracy of 86.7 . Automatic drum transcription methods aim at extracting a symbolic representation of notes played by a drum kit in audio recordings. For automatic music analysis, this task is of particular interest as such a transcript can be used to extract high level information about the piece, e.g., tempo, downbeat positions, meter, and genre cues. In this work, an approach to transcribe drums from polyphonic audio signals based on a recurrent neural network is presented. Deep learning techniques like dropout and data augmentation are applied to improve the generalization capabilities of the system. The method is evaluated using established reference datasets consisting of solo drum tracks as well as drums mixed with accompaniment. The results are compared to state-of-the-art approaches on the same datasets. The evaluation reveals that F-measure values higher than state of the art can be achieved using the proposed method. We present Tony, a software tool for the interactive annotation of melodies from monophonic audio recordings, and evaluate its usability and the accuracy of its note extraction method. The scientific study of acoustic performances of melodies, whether sung or played, requires the accurate transcription of notes and pitches. To achieve the desired transcription accuracy for a particular application, researchers manually correct results obtained by automatic methods. Tony is an interactive tool directly aimed at making this correction task efficient. It provides (a) state-of-the art algorithms for pitch and note estimation, (b) visual and auditory feedback for easy error-spotting, (c) an intelligent graphical user interface through which the user can rapidly correct estimation errors, (d) extensive export functions enabling further processing in other applications. We show that Tony’s built in automatic note transcription method compares favourably with existing tools. We report how long it takes to annotate recordings on a set of 96 solo vocal recordings and study the effect of piece, the number of edits made and the annotator’s increasing mastery of the software. Tony is Open Source software, with source code and compiled binaries for Windows, Mac OS X and Linux available from https: code.soundsoftware.ac.uk projects tony . A new algorithm is proposed for robust principal component analysis with predefined sparsity patterns. The algorithm is then applied to separate the singing voice from the instrumental accompaniment using vocal activity information. To evaluate its performance, we construct a new publicly available iKala dataset that features longer durations and higher quality than the existing MIR-1K dataset for singing voice separation. Part of it will be used in the MIREX Singing Voice Separation task. Experimental results on both the MIR-1K dataset and the new iKala dataset confirmed that the more informed the algorithm is, the better the separation results are. We present a discriminative model for polyphonic piano transcription. Support vector machines trained on spectral features are used to classify frame-level note instances. The classifier outputs are temporally constrained via hidden Markov models, and the proposed system is used to transcribe both synthesized and real piano recordings. A frame-level transcription accuracy of 68 was achieved on a newly generated test set, and direct comparisons to previous approaches are provided. This paper proposes an algorithm for aligning singing in pol yphonic music audio with textual lyrics. As preprocessing, t he system uses a voice separation algorithm based on melody transcription and sinusoidal modeling. The alignment is based on a hidden Markov model speech recognizer where the acoustic model is adapted to singing voice. The textual input is preprocess ed to create a language model consisting of a sequence of phonemes, pauses and possible instrumental breaks. Viterbi algorith m is used to align the audio features with the text. On a test set consis ting of 17 commercial recordings, the system achieves an average absolute error of 1.40 seconds in aligning lines of the lyrics. Monaural singing voice separation has aroused considerable attention. Many pitch-based methods have been proposed to address this task, but generally have limited performance. The most crucial difficulties lie in the inaccurate judgment on voiced pitches and the failed recognition on unvoiced singing sounds. In this paper, we propose a novel algorithm based on the latent component analysis of time-frequency representation to overcome these difficulties. Specifically, the time-frequency (T-F) representations of the song are firstly decomposed into components, and each component approximately originates from a single sound source. We then construct non-overlapping T-F segments with these components, to complete the omitted useful singing voice information. Extensive experiments on the MIR-1K public dataset shows the effectiveness of the proposed algorithm. The paper considers the task of recognizing phonemes and words from a singing input by using a phonetic hidden Markov model recognizer. The system is targeted to both monophonic singing and singing in polyphonic music. A vocal separation algorithm is applied to separate the singing from polyphonic music. Due to the lack of annotated singing databases, the recognizer is trained using speech and linearly adapted to singing. Global adaptation to singing is found to improve singing recognition performance. Further improvement is obtained by gender-specific adaptation. We also study adaptation with multiple base classes defined by either phonetic or acoustic similarity. We test phoneme-level and word-level n-gram language models. The phoneme language models are trained on the speech database text. The large-vocabulary word-level language model is trained on a database of textual lyrics. Two applications are presented. The recognizer is used to align textual lyrics to vocals in polyphonic music, obtaining an average error of 0.94 seconds for line-level alignment. A query-by-singing retrieval application based on the recognized words is also constructed; in 57 of the cases, the first retrieved song is the correct one. We propose a new approach to instrument recognition in the context of real music orchestrations ranging from solos to quartets. The strength of our approach is that it does not require prior musical source separation. Thanks to a hierarchical clustering algorithm exploiting robust probabilistic distances, we obtain a taxonomy of musical ensembles which is used to efficiently classify possible combinations of instruments played simultaneously. Moreover, a wide set of acoustic features is studied including some new proposals. In particular, signal to mask ratios are found to be useful features for audio classification. This study focuses on a single music genre (i.e., jazz) but combines a variety of instruments among which are percussion and singing voice. Using a varied database of sound excerpts from commercial recordings, we show that the segmentation of music with respect to the instruments played can be achieved with an average accuracy of 53 . This paper presents a novel statistical singing voice conversion (SVC) technique with direct waveform modification based on the spectrum differential that can convert voice timbre of a source singer into that of a target singer without using a vocoder to generate converted singing voice waveforms. SVC makes it possible to convert singing voice characteristics of an arbitrary source singer into those of an arbitrary target singer. However, speech quality of the converted singing voice is significantly degraded compared to that of a natural singing voice due to various factors, such as analysis and modeling errors in the vocoderbased framework. To alleviate this degradation, we propose a statistical conversion process that directly modifies the signal in the waveform domain by estimating the difference in the spectra of the source and target singers’ singing voices. The differential spectral feature is directly estimated using a differential Gaussian mixture model (GMM) that is analytically derived from the traditional GMM used as a conversion model in the conventional SVC. The experimental results demonstrate that the proposed method makes it possible to significantly improve speech quality in the converted singing voice while preserving the conversion accuracy of singer identity compared to the conventional SVC. Index Terms: singing voice, statistical voice conversion, vocoder, Gaussian mixture model, differential spectral compensation This work address the application of Voice Conversion to singing-voice. The GMM-based approach was applied to VOCALOID, a concatenative singing synthesizer, to perform singer timbre conversion. The conversion framework was applied to full-quality singing databases, achieving a satisfactory conversion effect on the synthesized utterances. We report in this paper the results of our experimentation focused to study the spectral conversion performance when applied to specific pitch-range data. 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. This paper presents techniques to improve the quality of voices generated through statistical singing voice conversion with direct waveform modification based on spectrum differential (DIFFSVC). The DIFFSVC method makes it possible to convert singing voice characteristics of a source singer into those of a target singer without using vocoder-based waveform generation. However, quality of the converted singing voice still degrades compared to that of a natural singing voice due to various factors, such as the over-smoothing of the converted spectral parameter trajectory. To alleviate this over-smoothing, we propose a technique to restore the global variance of the converted spectral parameter trajectory within the framework of the DIFFSVC method. We also propose another technique to specifically avoid over-smoothing at unvoiced frames. Results of subjective and objective evaluations demonstrate that the proposed techniques significantly improve speech quality of the converted singing voice while preserving the conversion accuracy of singer identity compared to the conventional DIFFSVC. Singing voice conversion is a task to convert a song sang by a source singer to the voice of a target singer. In this paper, we propose using a parallel data free, many-to-one voice conversion technique on singing voices. A phonetic posterior feature is first generated by decoding singing voices through a robust Automatic Speech Recognition Engine (ASR). Then, a trained Recurrent Neural Network (RNN) with a Deep Bidirectional Long Short Term Memory (DBLSTM) structure is used to model the mapping from person-independent content to the acoustic features of the target person. F0 and aperiodic are obtained through the original singing voice, and used with acoustic features to reconstruct the target singing voice through a vocoder. In the obtained singing voice, the targeted and sourced singers sound similar. To our knowledge, this is the first study that uses non parallel data to train a singing voice conversion system. Subjective evaluations demonstrate that the proposed method effectively converts singing voices. This paper describes a system that can automatically synchronize polyphonic musical audio signals with their corresponding lyrics. Although methods for synchronizing monophonic speech signals and corresponding text transcriptions by using Viterbi alignment techniques have been proposed, these methods cannot be applied to vocals in CD recordings because vocals are often overlapped by accompaniment sounds. In addition to a conventional method for reducing the influence of the accompaniment sounds, we therefore developed four methods to overcome this problem: a method for detecting vocal sections, a method for constructing robust phoneme networks, a method for detecting fricative sounds, and a method for adapting a speech-recognizer phone model to segregated vocal signals. We then report experimental results for each of these methods and also describe our music playback interface that utilizes our system for synchronizing music and lyrics. Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects. Recentlyunsupervisedfeaturelearningmethodshaveshown great promise as a way of extracting features from high dimensional data, such as image or audio. In this paper, we apply deep belief networks to musical data and evaluate the learned feature representations on classification-based polyphonic piano transcription. We also suggest a way of training classifiers jointly for multiple notes to improve training speed and classification performance. Our method is evaluated on three public piano datasets. The results show that the learned features outperform the baseline features, and also our method gives significantly better frame-level accuracy than other state-of-the-art music transcription methods. This paper introduces a new score-informed method for the segmentation of jingju a cappella singing phrase into syllables. The proposed method estimates the most likely sequence of syllable boundaries given the estimated syllable onset detection function (ODF) and its score. Throughout the paper, we first examine the jingju syllables structure and propose a definition of the term "syllable onset". Then, we identify which are the challenges that jingju a cappella singing poses. Further, we investigate how to improve the syllable ODF estimation with convolutional neural networks (CNNs). We propose a novel CNN architecture that allows to efficiently capture different time-frequency scales for estimating syllable onsets. In addition, we propose using a score-informed Viterbi algorithm -instead of thresholding the onset function-, because the available musical knowledge we have (the score) can be used to inform the Viterbi algorithm in order to overcome the identified challenges. The proposed method outperforms the state-of-the-art in syllable segmentation for jingju a cappella singing. We further provide an analysis of the segmentation errors which points possible research directions.