Pati, Kumar Ashis; Gururani, Siddharth; Lerch, Alexander Assessment of Student Music Performances Using Deep Neural Networks Journal Article In: Applied Sciences, 8 (4), pp. 507, 2018. Abstract | Links | BibTeX | Tags: deep learning, deep neural networks, DNN, MIR, music education, music informatics, music information retrieval, music learning, music performance assessment Lu, Yen-Cheng; Wu, Chih-Wei; Lu, Chang-Tien; Lerch, Alexander An Unsupervised Approach to Anomaly Detection in Music Datasets Inproceedings In: Proceedings of the ACM SIGIR Conference (SIGIR), pp. 749–752, ACM, Pisa, 2016, ISBN: 978-1-4503-4069-4. Abstract | Links | BibTeX | Tags: anomaly detection, data clean-up, music genre retrieval, music information retrieval Lu, Yen-Cheng; Wu, Chih-Wei; Lu, Chang-Tien; Lerch, Alexander Automatic Outlier Detection in Music Genre Datasets Inproceedings In: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), ISMIR, New York, 2016. Abstract | Links | BibTeX | Tags: anomaly detection, data clean-up, music genre retrieval, music information retrieval Lerch, Alexander An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics Book Wiley-IEEE Press, Hoboken, 2012, ISBN: 978-1-118-26682-3. Abstract | Links | BibTeX | Tags: analysis, audio, audio signal processing, information, listening, machine, machine listening, music, music analysis, music information retrieval, processing, retrieval, signal2018
@article{pati_assessment_2018,
title = {Assessment of Student Music Performances Using Deep Neural Networks},
author = {Kumar Ashis Pati and Siddharth Gururani and Alexander Lerch},
url = {http://www.mdpi.com/2076-3417/8/4/507/pdf},
doi = {10.3390/app8040507},
year = {2018},
date = {2018-01-01},
urldate = {2018-03-27},
journal = {Applied Sciences},
volume = {8},
number = {4},
pages = {507},
abstract = {Music performance assessment is a highly subjective task often relying on experts to gauge both the technical and aesthetic aspects of the performance from the audio signal. This article explores the task of building computational models for music performance assessment, i.e., analyzing an audio recording of a performance and rating it along several criteria such as musicality, note accuracy, etc. Much of the earlier work in this area has been centered around using hand-crafted features intended to capture relevant aspects of a performance. However, such features are based on our limited understanding of music perception and may not be optimal. In this article, we propose using Deep Neural Networks (DNNs) for the task and compare their performance against a baseline model using standard and hand-crafted features. We show that, using input representations at different levels of abstraction, DNNs can outperform the baseline models across all assessment criteria. In addition, we use model analysis techniques to further explain the model predictions in an attempt to gain useful insights into the assessment process. The results demonstrate the potential of using supervised feature learning techniques to better characterize music performances.},
keywords = {deep learning, deep neural networks, DNN, MIR, music education, music informatics, music information retrieval, music learning, music performance assessment},
pubstate = {published},
tppubtype = {article}
}
2016
@inproceedings{lu_unsupervised_2016,
title = {An Unsupervised Approach to Anomaly Detection in Music Datasets},
author = {Yen-Cheng Lu and Chih-Wei Wu and Chang-Tien Lu and Alexander Lerch},
url = {http://www.musicinformatics.gatech.edu/wp-content_nondefault/uploads/2016/07/Lu-et-al_2016_An-Unsupervised-Approach-to-Anomaly-Detection-in-Music-Datasets.pdf},
doi = {10.1145/2911451.2914700},
isbn = {978-1-4503-4069-4},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the ACM SIGIR Conference (SIGIR)},
pages = {749--752},
publisher = {ACM},
address = {Pisa},
series = {SIGIR '16},
abstract = {This paper presents an unsupervised method for systematically identifying anomalies in music datasets. The model integrates categorical regression and robust estimation techniques to infer anomalous scores in music clips. When applied to a music genre recognition dataset, the new method is able to detect corrupted, distorted, or mislabeled audio samples based on commonly used features in music information retrieval. The evaluation results show that the algorithm outperforms other anomaly detection methods and is capable of finding problematic samples identified by human experts. The proposed method introduces a preliminary framework for anomaly detection in music data that can serve as a useful tool to improve data integrity in the future.},
keywords = {anomaly detection, data clean-up, music genre retrieval, music information retrieval},
pubstate = {published},
tppubtype = {inproceedings}
}
@inproceedings{lu_automatic_2016,
title = {Automatic Outlier Detection in Music Genre Datasets},
author = {Yen-Cheng Lu and Chih-Wei Wu and Chang-Tien Lu and Alexander Lerch},
url = {http://www.musicinformatics.gatech.edu/wp-content_nondefault/uploads/2016/07/Lu-et-al_2016_Automatic-Outlier-Detection-in-Music-Genre-Datasets.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {Proceedings of the International Society for Music Information Retrieval Conference (ISMIR)},
publisher = {ISMIR},
address = {New York},
series = {ISMIR},
abstract = {Outlier detection, also known as anomaly detection, is an
importanttopicthathasbeenstudiedfordecades. Anoutlier
detection system is able to identify anomalies in a dataset
and thus improve data integrity by removing the detected
outliers. It has been successfully applied to different types
of data in various fields such as cyber-security, finance,
and transportation. In the field of Music Information Re-
trieval (MIR), however, the number of related studies is
small. In this paper, we introduce different state-of-the-art
outlier detection techniques and evaluate their viability in
the context of music datasets. More specifically, we present
a comparative study of 6 outlier detection algorithms ap-
plied to a Music Genre Recognition (MGR) dataset. It is
determined how well algorithms can identify mislabeled or
corrupted files, and how much the quality of the dataset can
be improved. Results indicate that state-of-the-art anomaly
detection systems have problems identifying anomalies in
MGR datasets reliably.},
keywords = {anomaly detection, data clean-up, music genre retrieval, music information retrieval},
pubstate = {published},
tppubtype = {inproceedings}
}
importanttopicthathasbeenstudiedfordecades. Anoutlier
detection system is able to identify anomalies in a dataset
and thus improve data integrity by removing the detected
outliers. It has been successfully applied to different types
of data in various fields such as cyber-security, finance,
and transportation. In the field of Music Information Re-
trieval (MIR), however, the number of related studies is
small. In this paper, we introduce different state-of-the-art
outlier detection techniques and evaluate their viability in
the context of music datasets. More specifically, we present
a comparative study of 6 outlier detection algorithms ap-
plied to a Music Genre Recognition (MGR) dataset. It is
determined how well algorithms can identify mislabeled or
corrupted files, and how much the quality of the dataset can
be improved. Results indicate that state-of-the-art anomaly
detection systems have problems identifying anomalies in
MGR datasets reliably.2012
@book{lerch_introduction_2012,
title = {An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics},
author = {Alexander Lerch},
url = {http://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266785},
isbn = {978-1-118-26682-3},
year = {2012},
date = {2012-01-01},
publisher = {Wiley-IEEE Press},
address = {Hoboken},
abstract = {With the proliferation of digital audio distribution over digital media, audio content analysis is fast becoming a requirement for designers of intelligent signal-adaptive audio processing systems. Written by a well-known expert in the field, this book provides quick access to different analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike. A review of relevant fundamentals in audio signal processing, psychoacoustics, and music theory, as well as downloadable MATLAB files are also included. Please visit the companion website: www.AudioContentAnalysis.org},
keywords = {analysis, audio, audio signal processing, information, listening, machine, machine listening, music, music analysis, music information retrieval, processing, retrieval, signal},
pubstate = {published},
tppubtype = {book}
}
publications
Assessment of Student Music Performances Using Deep Neural Networks Journal Article In: Applied Sciences, 8 (4), pp. 507, 2018. An Unsupervised Approach to Anomaly Detection in Music Datasets Inproceedings In: Proceedings of the ACM SIGIR Conference (SIGIR), pp. 749–752, ACM, Pisa, 2016, ISBN: 978-1-4503-4069-4. Automatic Outlier Detection in Music Genre Datasets Inproceedings In: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), ISMIR, New York, 2016. An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics Book Wiley-IEEE Press, Hoboken, 2012, ISBN: 978-1-118-26682-3.2018
2016
2012