Indirect Acquisition of Violin Instrumental Controls from Audio Signal with Hidden Markov Models

Carrillo, Alfonso Perez; Wanderley, Marcelo M.

doi:10.1109/taslp.2015.2410140

Cited by 15 publications

(4 citation statements)

References 18 publications

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“…At last, the outcomes using the system have represented a feasible substitute for existing transcription approaches. In 2015, Carrillo and Marcelo [41] suggested a new indirect acquirement technique for estimating constant violin controls with a database of formerly recorded violin performance. The numerical techniques deployed were HMM with Multivariate GM.…”

Section: Classic and Jazzmentioning

confidence: 99%

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

Sagar Latake

2024

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In music, transcription is the practice of notating a piece or a sound which was previously unnotated and/or unpopular as a written music". An absolute transcription will be performed only when the timing, pitching, and instruments of all sound events are solved. In music transcription systems, a MIDI file is found to be a suitable format for melodic notations. This survey intends to make a review of 65 papers that concern music transcription using machine learning techniques. Accordingly, systematic analyses of the adopted techniques are carried out and presented briefly. The performances and related maximum achievements of each contribution are also portrayed in this survey. Moreover, the various datasets used in music transcription techniques were considered and reviewed in this work. Finally, the survey portrays the research problems and weaknesses that may be supportive for researchers to introduce the latest techniques related to music transcription.

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Section: Classic and Jazzmentioning

confidence: 99%

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

Sagar Latake

2024

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“…IPT creates a variety of spectral and temporal variations of the sounds in different instruments. Recent research has attempted to transcribe IPT on drum [9], erhu [10], guitar [11], [12] and violin [13], [14]. Hand-crafted features are commonly designed based on instrument acoustics to capture the salient variations induced by IPT.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning for Piano Sustain-Pedal Detection

Liang

Fazekas

Sandler

2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Detecting piano pedalling techniques in polyphonic music remains a challenging task in music information retrieval. While other piano-related tasks, such as pitch estimation and onset detection, have seen improvement through applying deep learning methods, little work has been done to develop deep learning models to detect playing techniques. In this paper, we propose a transfer learning approach for the detection of sustainpedal techniques, which are commonly used by pianists to enrich the sound. In the source task, a convolutional neural network (CNN) is trained for learning spectral and temporal contexts when the sustain pedal is pressed using a large dataset generated by a physical modelling virtual instrument. The CNN is designed and experimented through exploiting the knowledge of piano acoustics and physics. This can achieve an accuracy score of 0.98 in the validation results. In the target task, the knowledge learned from the synthesised data can be transferred to detect the sustain pedal in acoustic piano recordings. A concatenated feature vector using the activations of the trained convolutional layers is extracted from the recordings and classified into frame-wise pedal press or release. We demonstrate the effectiveness of our method in acoustic piano recordings of Chopin's music. From the crossvalidation results, the proposed transfer learning method achieves an average F-measure of 0.89 and an overall performance of 0.84 obtained using the micro-averaged F-measure. These results outperform applying the pre-trained CNN model directly or the model with a fine-tuned last layer.

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“…The acquisition of control parameters from musical performances is generally carried out either directly by measuring with sensors or indirectly by o↵-line analysis of an audio signal (Pérez-Carrillo and Wanderley, 2015;Wanderley and Depalle, 2004). In the case of the indirect acquisition, di↵erent methods allow for the extraction of the plucking position and fingering based on frequencydomain analysis techniques (Traube and Depalle, 2003;Traube and Smith, 2000) and time-domain approaches (Penttinen and Välimäki, 2004).…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of Guitar Fingering and Plucking Controls Based on Multimodal Analysis of Motion, Audio and Musical Score

Carrillo

Arcos

Wanderley

2016

Lecture Notes in Computer Science

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Abstract. This work presents a method for the extraction of instrumental controls during guitar performances. The method is based on the analysis of multimodal data consisting of a combination of motion capture, audio analysis and musical score. High speed video cameras based on marker identification are used to track the position of finger bones and articulations and audio is recorded with a transducer measuring vibration on the guitar body. The extracted parameters are divided into left hand controls, i.e. fingering (which string and fret is pressed with a left hand finger) and right hand controls, i.e. the plucked string, the plucking finger and the characteristics of the pluck (position, velocity and angles with respect to the string). Controls are estimated based on probability functions of low level features, namely, the plucking instants (i.e. note onsets), the pitch and the distances of the fingers (both hands) to strings and frets. Note onsets are detected via audio analysis, the pitch is extracted from the score and distances are computed from 3D Euclidean Geometry. Results show that by combination of multimodal information, it is possible to estimate such a comprehensive set of control features, with special high performance for the fingering and plucked string estimation. Regarding the plucking finger and the pluck characteristics, their accuracy gets lower but improvements are foreseen including a hand model and the use of high-speed cameras for calibration and evaluation.

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Indirect Acquisition of Violin Instrumental Controls from Audio Signal with Hidden Markov Models

Cited by 15 publications

References 18 publications

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

A Comprehensive Review on Automatic Music Transcription: Survey of Transcription Techniques

Transfer Learning for Piano Sustain-Pedal Detection

Estimation of Guitar Fingering and Plucking Controls Based on Multimodal Analysis of Motion, Audio and Musical Score

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