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Music Acquisition and Automatic Processing for Machine Learning-Based Applications
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Music Acquisition and Automatic Processing for Machine Learning-Based Applications

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 4247

Special Issue Editor

Dr. Alicja Wieczorkowska

E-Mail Website
Guest Editor
Multimedia Department, Polish-Japanese Academy of Information Technology, Warsaw, Poland
Interests: multimedia; audio signal analysis; music information retrieval; knowledge discovery in databases; data mining; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Music is present in our everyday lives. We can enjoy listening to music, which is available in our private and online collections; we can attend live concerts; we can also sing karaoke; or even rearrange or compose music pieces. Various apps can support all music-related activities.

This Special Issue of the Journal Sensors is focused on original research on the acquisition of audio signal for music applications, as well as on music processing using machine learning. The goal is to collect a diverse set of papers that span a wide range of hardware and software development processes for applications dedicated to music. Papers focused on the application of deep learning in music apps are also welcomed.

Dr. Alicja Wieczorkowska
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • signal acquisition
  • digital signal processing
  • audio signal analysis
  • sensors
  • machine learning

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Published Papers (3 papers)

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Research

21 pages, 2964 KB  
Article
MEMA: Multimodal Aesthetic Evaluation of Music in Visual Contexts
by Huaye Zhang, Chenglizhao Chen, Mengke Song, Tingting Chen, Diqiong Jiang, Lichun Liu and Xinyu Liu
Sensors 2026, 26(4), 1395; https://doi.org/10.3390/s26041395 - 23 Feb 2026
Viewed by 745
Abstract
Recent technologies such as music retrieval, soundtrack generation, and video understanding have developed rapidly. Consequently, the aesthetic evaluation of video soundtracks has become an important research topic in academia. Soundtracks are key elements in shaping the emotional atmosphere and driving the narrative rhythm. [...] Read more.
Recent technologies such as music retrieval, soundtrack generation, and video understanding have developed rapidly. Consequently, the aesthetic evaluation of video soundtracks has become an important research topic in academia. Soundtracks are key elements in shaping the emotional atmosphere and driving the narrative rhythm. Therefore, they require systematic methods to assess their artistic coordination with visual content. However, existing approaches mostly focus on evaluating the quality of the music itself. They often lack the ability to model the deeper aesthetic synergy between audio and visuals. To address this gap, we propose MEMA, a new soundtrack aesthetic evaluation model. MEMA employs a two-stage training strategy. The first stage builds a crossmodal imagination mechanism using a Conditional Variational Autoencoder. This method achieves bidirectional semantic reconstruction between audio and visuals. The second stage introduces a Guided Cross-Attention Alignment Module. This module enhances the model’s focus on key narrative moments in video. To facilitate this research, we also construct VMAE-Sets. It is the first large-scale dataset dedicated to soundtrack aesthetic evaluation. Finally, MEMA performs scoring and textual evaluation along three core aesthetic dimensions. Experimental results demonstrate that MEMA outperforms existing methods, achieving average improvements of 18.137% in LCC and 17.866% in SRCC compared to the strongest baseline. These findings confirm its superior audio–visual narrative alignment, demonstrating high consistency with human judgments. Full article
(This article belongs to the Special Issue Music Acquisition and Automatic Processing for Machine Learning-Based Applications)
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20 pages, 2555 KB  
Article
Joint Learning of Emotion and Singing Style for Enhanced Music Style Understanding
by Yuwen Chen, Jing Mao and Rui-Feng Wang
Sensors 2025, 25(24), 7575; https://doi.org/10.3390/s25247575 - 13 Dec 2025
Viewed by 756
Abstract
Understanding music styles is essential for music information retrieval, personalized recommendation, and AI-assisted content creation. However, existing work typically addresses tasks such as emotion classification and singing style classification independently, thereby neglecting the intrinsic relationships between them. In this study, we introduce a [...] Read more.
Understanding music styles is essential for music information retrieval, personalized recommendation, and AI-assisted content creation. However, existing work typically addresses tasks such as emotion classification and singing style classification independently, thereby neglecting the intrinsic relationships between them. In this study, we introduce a multi-task learning framework that jointly models these two tasks to enable explicit knowledge sharing and mutual enhancement. Our results indicate that joint optimization consistently outperforms single-task counterparts, demonstrating the value of leveraging inter-task correlations for more robust singing style analysis. To assess the generality and adaptability of the proposed framework, we evaluate it across various backbone architectures, including Transformer, TextCNN, and BERT, and observe stable performance improvements in all cases. Experiments on a benchmark dataset, which were self-constructed and collected through professional recording devices, further show that the framework not only achieves the best accuracy on both tasks on our dataset under a singer-wise split, but also yields interpretable insights into the interplay between emotional expression and stylistic characteristics in vocal performance. Full article
(This article belongs to the Special Issue Music Acquisition and Automatic Processing for Machine Learning-Based Applications)
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25 pages, 6152 KB  
Article
Change in Acoustic Parameters of Electric Guitar Strings Under Dynamic Loading
by Jakub Grzybowski, Piotr Wrzeciono and Hydayatullah Bayat
Sensors 2025, 25(13), 3989; https://doi.org/10.3390/s25133989 - 26 Jun 2025
Viewed by 1979
Abstract
The aim of our work was to investigate how electric guitar strings wear out. There are many myths about string wear. We decided to investigate what the wear process looks like in real life. In our work, sound processing methods such as DTFT [...] Read more.
The aim of our work was to investigate how electric guitar strings wear out. There are many myths about string wear. We decided to investigate what the wear process looks like in real life. In our work, sound processing methods such as DTFT and spectrogram were used. However, the most important research method is the use of time-frequency analysis to study the sound of the string and its wear process. Another key method used in our work is the application of a phenomenon known from psychoacoustics, pitch. In our work, we have been able to show that the use of pitch in combination with time-frequency analysis makes it possible to demonstrate string wear. This was not achievable for previously known methods. We have also shown that the string yield limit is exceeded immediately when the strings are placed on the guitar neck. This affects the sound equation of the string. In this work, we have proposed a transformation of the classical string equation so that it correctly describes the sound of the string as it is worn. The research method we have developed, combining pitch and time-frequency analysis, could presumably be used in the future to study the wear and tear of other vibrating systems, such as bridges and viaducts. Full article
(This article belongs to the Special Issue Music Acquisition and Automatic Processing for Machine Learning-Based Applications)
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