Evaluation Framework for Automatic Singing Transcription
@inproceedings{Molina2014EvaluationFF, title={Evaluation Framework for Automatic Singing Transcription}, author={Emilio Molina and Ana M. Barbancho and Lorenzo J. Tard{\'o}n and Isabel Barbancho}, booktitle={International Society for Music Information Retrieval Conference}, year={2014} }
In this paper, we analyse the evaluation strategies used in previous works on automatic singing transcription, and we present a novel, comprehensive and freely available evaluation framework for automatic singing transcription. This framework consists of a cross-annotated dataset and a set of extended evaluation measures, which are integrated in a Matlab toolbox. The presented evaluation measures are based on standard MIREX note-tracking measures, but they provide extra information about the…
36 Citations
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References
SHOWING 1-10 OF 18 REFERENCES
Towards Computer-Assisted Flamenco Transcription: An Experimental Comparison of Automatic Transcription Algorithms as Applied to A Cappella Singing
- Computer ScienceComputer Music Journal
- 2013
A transcription system based on fundamental frequency and energy estimation, which incorporates an iterative strategy for note segmentation and labeling is proposed, which outperforms a state-of-the-art approach designed for other singing styles.
Explicit Transition Modelling for Automatic Singing Transcription
- Computer Science
- 2008
A system for the automatic transcription of solo human singing into note sequences and Hidden Markov models 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.
An Auditory Model Based Transcriber of Singing Sequences
- Computer ScienceISMIR
- 2002
A new system for the automatic transcription of singing sequences into a sequence of pitch and duration pairs is presented and it is shown that the accuracy of the newly proposed transcription system is not very to the choice of the free parameters, at least as long as they remain in the vicinity of the values one could forecast on the basis of their meaning.
Recent improvements of an auditory model based front-end for the transcription of vocal queries
- Computer Science2004 IEEE International Conference on Acoustics, Speech, and Signal Processing
- 2004
Experiments have shown that the new system can transcribe vocal queries with an accuracy ranging from 76 % (whistling) to 85 % (humming), and that it clearly outperforms other state-of-the art systems on all three query types.
Fundamental frequency alignment vs. note-based melodic similarity for singing voice assessment
- Computer Science2013 IEEE International Conference on Acoustics, Speech and Signal Processing
- 2013
The results show that the proposed system is suitable for automatic singing voice rating and that DTW based measures are specially simple and effective for intonation and rhythm assessment.
Modelling of note events for singing transcription
- Computer ScienceSAPA@INTERSPEECH
- 2004
The method produces symbolic notations from acoustic inputs based on two probabilistic models: a note event model and a musicological model which form a melody transcription system with a modular architecture which can be extended with desired front-end feature extractors and musicological rules.
Sung Note Segmentation for a Query-by-Humming System
- Computer Science
- 2007
New acoustic feats based on the signal energy distribution as obtained from the singing pe rception and production points of view are investigated and a specific mid-band energy combined with a biphasic detection function achieves high co-rect detection and low false alarm rates on the sonorant consonant syllables.
An Audio Front End for Query-by-Humming Systems
- Computer ScienceISMIR
- 2001
A front end dedicated to the symbolic translation of voice into a sequence of pitch and duration pairs is developed, crucial for the effectiveness of searching for music by melodic similarity.
Probabilistic models for the transcription of single-voice melodies
- Computer Science
- 2003
A method is proposed for the automatic transcription of single-voice melodies from an acoustic waveform into a symbolic musical notation (a MIDI file) using a probabilistic model that handles imperfections in the performed/estimated pitch values using a hidden Markov model.
Automatic Transcription of Melody, Bass Line, and Chords in Polyphonic Music
- ArtComputer Music Journal
- 2008
The method is computationally efficient and allows causal implementation, so it can process streaming audio, and may be used in music analysis, music information retrieval from large music databases, content-based audio processing, and interactive music systems.