A position as student assistant for the support in teaching activities and empirical studies for optimisation of music data analysis using Artiﬁcial Intelligence is open and should be filled as soon as possible. For details, please see the PDF.
The 13th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) will take place on 3-5 April 2024, as part of the evo* event.
EvoMUSART webpage: www.evostar.org/2024/evomusart/
Submission deadline: 1 November 2023
Conference: 3-5 April 2024
EvoMUSART is a multidisciplinary conference that brings together researchers who are working on the application of Artificial Neural Networks, Evolutionary Computation, Swarm Intelligence, Cellular Automata, Alife, and other Artificial Intelligence techniques in creative and artistic fields such as Visual Art, Music, Architecture, Video, Digital Games, Poetry, or Design. This conference gives researchers in the field the opportunity to promote, present and discuss ongoing work in the area.
Submissions must be at most 14 pages long, excluding references, in Springer LNCS format. Each submission must be anonymized for a double-blind review process.
Accepted papers will be presented orally or as posters at the event and included in the EvoMUSART proceedings published by Springer Nature in a dedicated volume of the Lecture Notes in Computer Science series.
More information on the submission process and the topics of EvoMUSART: www.evostar.org/2024/evomusart/
Flyer of EvoMUSART 2024:
The program of the following 55th SIGMA meeting on 16.06.2023, 14:00-16:00, which takes place at the Chair of Algorithm Engineering, Deparment of Computer Science, TU Dortmund, Otto-Hahn-Str. 14, room 202, and online (please send an email to igor.vatolkin [at] udo.edu if you wish to get the Zoom link):
14:00-14:05 Welcome greetings
14:05-14:35 Bachelor’s thesis (results)
Alhuseen Ali: Comparison between Artificial Neural Networks and Traditional Classifiers for the Suppression of Background Noise
14:35-15:05 Research study
Leonard Fricke: Application of AugmentedNet for Different Sound Bodies
15:05-15:35 Conference study
Igor Vatolkin: Musical Genre Recognition based on Deep Neural Network Predictions of Harmony, Instrumentation, and Segments
15:35-15:55 Conferences and calls, miscellaneous, next meeting
Paper describing Artificial Audio Multitracks (AAM) dataset published in EURASIP Journal on Audio, Speech, and Music Processing
F. Ostermann, I. Vatolkin, and M. Ebeling: AAM: a Dataset of Artificial Audio Multitracks for Diverse Music Information Retrieval Tasks. EURASIP Journal on Audio, Speech, and Music Processing, 13, 2023.
Zenodo link: https://doi.org/10.5281/zenodo.5794629
Abstract: We present a new dataset of 3000 artificial music tracks with rich annotations based on real instrument samples and generated by algorithmic composition with respect to music theory. Our collection provides ground truth onset information and has several advantages compared to many available datasets. It can be used to compare and optimize algorithms for various music information retrieval tasks like music segmentation, instrument recognition, source separation, onset detection, key and chord recognition, or tempo estimation. As the audio is perfectly aligned to original MIDIs, all annotations (onsets, pitches, instruments, keys, tempos, chords, beats, and segment boundaries) are absolutely precise. Because of that, specific scenarios can be addressed, for instance, detection of segment boundaries with instrument and key change only, or onset detection only in tracks with drums and slow tempo. This allows for the exhaustive evaluation and identification of individual weak points of algorithms. In contrast to datasets with commercial music, all audio tracks are freely available, allowing for extraction of own audio features. All music pieces are stored as single instrument audio tracks and a mix track, so that different augmentations and DSP effects can be applied to extend training sets and create individual mixes, e.g., for deep neural networks. In three case studies, we show how different algorithms and neural network models can be analyzed and compared for music segmentation, instrument recognition, and onset detection. In future, the dataset can be easily extended under consideration of specific demands to the composition process.
On 03.02.2023, the last unit of the interdisciplinary lecture on Music Data Analysis was organized by Prof. Meinard Müller, Audiolabs Erlangen. The slides on FMP Notebooks are available here (handout version).
(1) I. Vatolkin, M. Gotham, N. Nápoles López, and F. Ostermann: Musical Genre Recognition based on Deep Descriptors of Harmony, Instrumentation, and Segments. Accepted for Proceedings of the 12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART)
Abstract: Deep learning has recently established itself as a cluster of methods of choice for almost all classification tasks in music information retrieval. However, despite very good classification performance, it sometimes brings disadvantages including long training times and higher energy costs, lower interpretability of classification models, or an increased risk of overfitting when applied to small training sets due to a very large number of trainable parameters. In this paper, we investigate the combination of both deep and shallow algorithms for recognition of musical genres using a transfer learning approach. We train deep classification models once to predict harmonic, instrumental, and segment properties from datasets with respective annotations. Their predictions for another dataset with annotated genres are used as features for shallow classification methods. They can be trained over and again for different categories, and are particularly useful when the training sets are small, in a real world scenario when listeners define various musical categories selecting only a few prototype tracks. The experiments show the potential of the proposed approach for genre recognition. In particular, when combined with evolutionary feature selection which identifies the most relevant deep feature dimensions, the classification errors became significantly lower in almost all cases, compared to a baseline based on MFCCs or results reported in the previous work.
(2) L. Fricke, I. Vatolkin, and F. Ostermann: Application of Neural Architecture Search to Instrument Recognition in Polyphonic Audio. Accepted for Proceedings of the 12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART)
Abstract: Instrument recognition in polyphonic audio signals is a very challenging classification task. It helps to improve related application scenarios, like music transcription and recommendation, organization of large music collections, or analysis of historical trends and properties of musical styles. Recently, the classification performance could be improved by the integration of deep convolutional neural networks. However, in to date published studies, the network architectures and parameter settings were usually adopted from image recognition tasks and manually adjusted, without a systematic optimization. In this paper, we show how two different neural architecture search strategies can be successfully applied for improvement of the prediction of nine instrument classes, significantly outperforming the classification performance of three fixed baseline architectures from previous works. Although high computing efforts for model optimization are required, the training of the final architecture is done only once for later prediction of instruments in a possibly unlimited number of musical tracks.
The program of the following 54th SIGMA meeting on 16.01.2023, 14:00-16:00, which takes place at the Chair of Algorithm Engineering, Deparment of Computer Science, TU Dortmund, Otto-Hahn-Str. 14, room 202, and online (please send an email to igor.vatolkin [at] udo.edu if you wish to get the Zoom link):
14:00-14:05 Welcome greetings
14:05-14:35 Master’s thesis (introduction)
Justin Dettmer: Expanding an Evolutionary Algorithm for the Synthesis of Polyphonic Music
14:35-15:05 Conference study
Igor Vatolkin: Stability of Symbolic Feature Group Importance in the Context of Multi-Modal Music Classification
15:05-15:35 Research study
Leonard Fricke: Neural Architecture Search for Instrument Recognition
15:35-15:55 Conferences and calls, miscellaneous, next meeting
For the fifth time, the interdisciplinary lecture „Music data analysis“ takes place at TU Dortmund (lecture on Fridays, 10-12, with excercises on Fridays, 12-13).
The 12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) will take place in Brno (Czech Republic) between the 12th and 14th of April of 2023, as part of the evo* event.
EvoMUSART webpage: https://www.evostar.org/2023/evomusart
Conference: 12 – 14 April 2023
EvoMUSART is a multidisciplinary conference that brings together researchers who are working on the application of Artificial Neural Networks, Evolutionary Computation, Swarm Intelligence, Cellular Automata, Alife, and other Artificial Intelligence techniques in creative and artist fields such as Visual Art, Music, Architecture, Video, Digital Games, Poetry, or Design. This conference gives researchers in the field the opportunity to promote, present and discuss ongoing work in the area.
More information on the submission process and the topics of EvoMUSART: https://www.evostar.org/2023/evomusart
I. Vatolkin and C. McKay: Stability of Symbolic Feature Group Importance in the Context of Multi-Modal Music Classification. accepted for Proceedings of the 23rd International Society for Music Information Retrieval Conference (ISMIR)
Abstract: Multi-modal music classification creates supervised models trained on features from different sources (modalities): the audio signal, the score, lyrics, album covers, expert tags, etc. A concept of “multi-group feature importance” not only helps to measure the individual relevance of features belonging to a feature type under investigation (such as the instruments present in a piece), but also serves to quantify the potential for further improving classification quality by adding features from other feature types or extracted from different kinds of sources, based on a multi-objective analysis of feature sets after evolutionary feature selection. In this study, we investigate the stability of feature group importance when different classification methods and different measures of classification quality are applied. Since musical scores are particularly helpful in deriving semantically meaningful, robust genre characteristics, we focus on the feature groups analyzed by the jSymbolic feature extraction software, which describe properties associated with instrumentation, basic pitch statistics, melody, chords, tempo, and other rhythmic aspects. These symbolic features are analyzed in the context of musical information drawn from five other modalities, and experiments are conducted involving two datasets, one small and one large. The results show that, although some feature groups can remain similarly important compared to others, differences can also be evident in various application cases, and can depend on the particular classifier and evaluation measure being used. Insights drawn from this type of analysis can potentially be helpful in effectively matching specific features or feature groups to particular classifiers and evaluation measures in future feature-based MIR research.