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 178932670 2012433269 2604244390
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.