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.
Approaches to automatic note-level transcription from audio recordings date back as far as 1977 @cite_37 and are extensively reviewed by @cite_18 and @cite_17 . Most systems described in these reviews provide a generic transcription framework covering a wide range of instruments and musical genres. As mentioned in @cite_17 , specific instruments exhibit particular characteristics which might not be captured by a generic transcription system.
@cite_37 @cite_18 @cite_17
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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. 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. 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. 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 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. 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. 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 . 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. 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... 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 . 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. Transcribing lyrics from musical audio is a challenging research problem which has not benefited from many advances made in the related field of automatic speech recognition, owing to the prevalent musical accompaniment and differences between the spoken and sung voice. However, one aspect of this problem which has yet to be exploited by researchers is that significant portions of the lyrics will be repeated throughout the song. In this paper we investigate how this information can be leveraged to form a consensus transcription with improved consistency and accuracy. Our results show that improvements can be gained using a variety of techniques, and that relative gains are largest under the most challenging and realistic experimental conditions. The use of multiple distant microphones has been widely studied for meeting recognition. The two most widely used approaches are 1) combination at the signal level, via blind beamforming, followed by recognition of a single enhanced audio signal, and 2) independent, logically parallel recognition of the multiple audio sources followed by hypothesis-level combination. In this paper we investigate how these two approaches compare for state-of-the-art recognition systems applied to meeting data from the two most recent NIST Rich Transcription evaluations. Our results show that beamforming is the superior approach, giving more accurate results while being inherently less computationally demanding. We then propose a hybrid approach that leverages both beamforming and signal-level diversity for systemcombination, and showthat this approach gives gains over either of the old methods. 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. In this work, we present our progress in multi-source far field automatic speech-to-text transcription for lecture speech. In particular, we show how the best of several far field channels can be selected based on a signal-to-noise ratio criterion, and how the signals from multiple channels can be combined at either the waveform level using blind channel combination or at the hypothesis level using confusion network techniques to improve the accuracy of a far field lecture transcription system. Using the techniques described here, we ran a series of experiments on the test set used by the US National Institute of Standards and Technologies for the RT-05S evaluation. For the multiple distant microphones (MDM) task of RT-05S, our system achieved a word error rate of 38.5 which represents an improvement of over 13 absolute compared to the best reported results in the RT-05S evaluation. Index Terms: far-distance, automatic speech recognition Research on automatic music generation has seen great progress due to the development of deep neural networks. However, the generation of multi-instrument music of arbitrary genres still remains a challenge. Existing research either works on lead sheets or multi-track piano-rolls found in MIDIs, but both musical notations have their limits. In this work, we propose a new task called lead sheet arrangement to avoid such limits. A new recurrent convolutional generative model for the task is proposed, along with three new symbolic-domain harmonic features to facilitate learning from unpaired lead sheets and MIDIs. Our model can generate lead sheets and their arrangements of eight-bar long. Source code and audio samples of the generated result can be found at the project webpage: https: liuhaumin. github.io LeadsheetArrangement This publication presents a new method for the detection and classification of un-pitched percussive instruments in real world musical signals. The derived information is an important pre-requisite for the creation of a musical score, i.e. automatic transcription, and for the automatic extraction of semantic meaningful meta-data, e.g. tempo and musical meter. The proposed method applies Independent Subspace Analysis using Non-Negative Independent Component Analysis and principles of Prior Subspace Analysis. An important extension of Prior Subspace Analysis is the identification of frequency subspaces of percussive instruments from the signal itself. The frequency subspaces serve as information for the detection of the percussive events and the subsequent classification of the occurring instruments. Results are reported on 40 manually transcribed test items. This paper explores a variety of models for frame-based music transcription, with an emphasis on the methods needed to reach state-of-the-art on human recordings. The translation-invariant network discussed in this paper, which combines a traditional filterbank with a convolutional neural network, was the top-performing model in the 2017 MIREX Multiple Fundamental Frequency Estimation evaluation. This class of models shares parameters in the log-frequency domain, which exploits the frequency invariance of music to reduce the number of model parameters and avoid overfitting to the training data. All models in this paper were trained with supervision by labeled data from the MusicNet dataset, augmented by random label-preserving pitch-shift transformations. We address the problem of estimating the quality of Automatic Speech Recognition (ASR) output at utterance level, without recourse to manual reference transcriptions and when information about system’s confidence is not accessible. Given a source signal and its automatic transcription, we approach this problem as a regression task where the word error rate of the transcribed utterance has to be predicted. To this aim, we explore the contribution of different feature sets and the potential of different algorithms in testing conditions of increasing complexity. Results show that our automatic quality estimates closely approximate the word error rate scores calculated over reference transcripts, outperforming a strong baseline in all the testing conditions.