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.
Addressing the more complex task of singing transcription from polyphonic recordings @cite_35 , a multiple fundamental frequency estimator with an accent signal in order to extract the dominant pitch trajectory. The segmentation stage relies on a probabilistic note event model, using a Hidden Markov Model (HMM) trained on manual transcriptions. In an extension to this approach @cite_14 , note transition probabilities were incorporated in the computational model. Recently, this note segmentation method was implemented in the computer-aided note transcription tool tony @cite_25 . It should be mentioned that such probabilistic methods require a large amount of ground truth data during the training stage. This involves time-consuming manual annotations in particular for improvisational and strongly ornamented singing performances, as it is the case for flamenco music.
@cite_35 @cite_14 @cite_25
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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. 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 . 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 . The aim of this overview is to describe methods for the automatic transcription of Western polyphonic music. The transcription task is here understood as transforming an acoustic musical signal into a MIDI-like symbolic representation. Only pitched musical instruments are considered: recognizing the sounds of drum instruments is not discussed. The main emphasis is laid on estimating the multiple fundamental frequencies of several concurrent sounds. Various approaches to solve this problem are discussed, including methods that are based on modelling the human auditory periphery, methods that mimic the human auditory scene analysis function, signal model-based Bayesian inference methods, and data-adaptive methods. Another subproblem addressed is the rhythmic parsing of acoustic musical signals. From the transcription point of view, this amounts to the temporal segmentation of music signals at different time scales. The relationship between the two subproblems and the general structure of the transcription p... Abstract This article presents a probabilistic model of polyphonic music analysis. Taking a note pattern as input, the model combines three aspects of symbolic music analysis—metrical analysis, harmonic analysis, and stream segregation—into a single process, allowing it to capture the complex interactions between these structures. The model also yields an estimate of the probability of the note pattern itself; this has implications for the modelling of music transcription. I begin by describing the generative process that is assumed and the analytical process that is used to infer metrical, harmonic, and stream structures from a note pattern. I then present some tests of the model on metrical analysis and harmonic analysis, and discuss ongoing work to integrate the model into a transcription system. This paper proposes a Bayesian approach for automatic music transcription of polyphonic MIDI signals based on generative modeling of onset occurrences of musical notes. Automatic music transcription involves two subproblems that are interdependent of each other: rhythm recognition and tempo estimation. When we listen to music, we are able to recognize its rhythm and tempo (or beat location) fairly easily even though there is ambiguity in determining the individual note values and tempo. This may be made possible through our empirical knowledge about rhythm patterns and tempo variations that possibly occur in music. To automate the process of recognizing the rhythm and tempo of music, we propose modeling the generative process of a MIDI signal of polyphonic music by combining the sub-process by which a musically natural tempo curve is generated and the sub-process by which a set of note onset positions is generated based on a 2-dimensional rhythm tree structure representation of music, and develop a parameter inference algorithm for the proposed model. We show some of the transcription results obtained with the present method. 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 discusses model-based rhythm and tempo analysis of music data in the MIDI format. The data is assumed to be obtained from a module performing multi-pitch analysis of music acoustic signals inside an automatic transcription system. In performed music, observed note lengths and local tempo fluctuate from the nominal note lengths and long-term tempo. Applying the framework of continuous speech recognition to rhythm recognition, we take a probabilistic top-down approach on the joint estimation of rhythm and tempo from the performed onset events in MIDI data. Short-term rhythm patterns are extracted from existing music samples and form a "rhythm vocabulary." Local tempo is represented by a smooth curve. The entire problem is formulated as an integrated optimization problem to maximize a posterior probability, which can be solved by an iterative algorithm which alternately estimates rhythm and tempo. Evaluation of the algorithm through various experiments is also presented. This paper describes a Hidden Markov Model (HMM)-based method of automatic transcription of MIDI (Musical Instrument Digital Interface) signals of performed music. The problem is formulated as recognition of a given sequence of fluctuating note durations to find the most likely intended note sequence utilizing the modern continuous speech recognition technique. Combining a stochastic model of deviating note durations and a stochastic grammar representing possible sequences of notes, the maximum likelihood estimate of the note sequence is searched in terms of Viterbi algorithm. The same principle is successfully applied to a joint problem of bar line allocation, time measure recognition, and tempo estimation. Finally, durations of consecutive spl eta n notes are combined to form a "rhythm vector" representing tempo-free relative durations of the notes and treated in the same framework. Significant improvements compared with conventional "quantization" techniques are shown. Retrieving short monophonic queries in music recordings is a challenging research problem in Music Information Retrieval (MIR). In jazz music, given a solo transcription, one retrieval task is to find the corresponding (potentially polyphonic) recording in a music collection. Many conventional systems approach such retrieval tasks by first extracting the predominant F0-trajectory from the recording, then quantizing the extracted trajectory to musical pitches and finally comparing the resulting pitch sequence to the monophonic query. In this paper, we introduce a data-driven approach that avoids the hard decisions involved in conventional approaches: Given pairs of time-frequency (TF) representations of full music recordings and TF representations of solo transcriptions, we use a DNN-based approach to learn a mapping for transforming a “polyphonic” TF representation into a “monophonic” TF representation. This transform can be considered as a kind of solo voice enhancement. We evaluate our approach within a jazz solo retrieval scenario and compare it to a state-of-the-art method for predominant melody extraction. 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. In a recent conference paper, we have reported a rhythm transcription method based on a merged-output hidden Markov model HMM that explicitly describes the multiple-voice structure of polyphonic music. This model solves a major problem of conventional methods that could not properly describe the nature of multiple voices as in polyrhythmic scores or in the phenomenon of loose synchrony between voices. In this paper, we present a complete description of the proposed model and develop an inference technique, which is valid for any merged-output HMMs, for which output probabilities depend on past events. We also examine the influence of the architecture and parameters of the method in terms of accuracies of rhythm transcription and voice separation and perform comparative evaluations with six other algorithms. Using MIDI recordings of classical piano pieces, we found that the proposed model outperformed other methods by more than 12 points in the accuracy for polyrhythmic performances and performed almost as good as the best one for non-polyrhythmic performances. This reveals the state-of-the-art methods of rhythm transcription for the first time in the literature. Publicly available source codes are also provided for future comparisons. This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models (HMMs). The task is to detect the temporal locations of unpitched percussive sounds (such as bass drum or hi-hat) and recognise the instruments played. Contrary to many earlier methods, a separate sound event segmentation is not done, but connected HMMs are used to perform the segmentation and recognition jointly. Two ways of using HMMs are studied: modelling combinations of the target drums and a detector-like modelling of each target drum. Acoustic feature parametrisation is done with mel-frequency cepstral coefficients and their first-order temporal derivatives. The effect of lowering the feature dimensionality with principal component analysis and linear discriminant analysis is evaluated. Unsupervised acoustic model parameter adaptation with maximum likelihood linear regression is evaluated for compensating the differences between the training and target signals. The performance of the proposed method is evaluated on a publicly available data set containing signals with and without accompaniment, and compared with two reference methods. The results suggest that the transcription is possible using connected HMMs, and that using detector-like models for each target drum provides a better performance than modelling drum combinations. We present a probabilistic generative model for timing deviations in expressive music performance. The structure of the proposed model is equivalent to a switching state space model. The switch variables correspond to discrete note locations as in a musical score. The continuous hidden variables denote the tempo. We formulate two well known music recognition problems, namely tempo tracking and automatic transcription (rhythm quantization) as filtering and maximum a posteriori (MAP) state estimation tasks. Exact computation of posterior features such as the MAP state is intractable in this model class, so we introduce Monte Carlo methods for integration and optimization. We compare Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated annealing and iterative improvement) and sequential Monte Carlo methods (particle filters). Our simulation results suggest better results with sequential methods. The methods can be applied in both online and batch scenarios such as tempo tracking and transcription and are thus potentially useful in a number of music applications such as adaptive automatic accompaniment, score typesetting and music information retrieval. While recent years have witnessed large progress in the algorithm of automatic music transcription (AMT), the development of general and sizable datasets for AMT evaluation is relatively stagnant, predominantly due to the fact that manually annotating and checking such datasets is labor-intensive and time-consuming. In this paper we propose a novel note-level annotation method for building AMT datasets by utilizing human’s ability in following music in real-time. To test the quality of the annotation, we further propose an efficient method in qualifying an AMT dataset based on the concepts of onset error difference and the tolerance computed from the evaluation result. According to the experiments on five piano solos and four woodwind quintets, we claim that the proposed annotation method is reliable for evaluation of AMT algorithms. 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. 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. Recent efforts in audio indexing and retrieval in music databases mostly focus on melody. If this is appropriate for polyphonic music signals, specific approaches are needed for systems dealing with percussive audio signals such as those produced by drums, tabla or djembe. Most studies of drum signal transcription focus on sounds taken in isolation. In this paper, we propose several methods for drum loop transcription where the drums signals dataset reflects the variability encountered in modern audio recordings (real and natural drum kits, audio effects, simultaneous instruments, etc.). The approaches described are based on hidden Markov models (HMM) and support vector machines (SVM). Promising results are obtained with a 83.9 correct recognition rate for a simplified taxonomy. We present a pattern recognition approach to the task of identifying performers from their interpretative styles. We investigate how professional musicians express their view of the musical content of musical pieces and how to use this information in order to automatically identify performers. We apply sound analysis techniques based on spectral models for extracting deviation patterns of parameters such as pitch, timing, amplitude and timbre characterising both the internal structure of notes and the musical context in which they appear. We describe successful performer identification case studies involving monophonic audio recordings of both score-guided and commercial improvised performances.