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17 pages, 3692 KB

Open AccessFeature PaperArticle

Wearable Haptic Music Player with Multi-Feature Extraction Using Spectral Flux and Yin Algorithms

by Aaron Benjmin R. Alcuitas, Thad Jacob T. Tiong, Hang-Hong Kuo and Aaron Raymond See

Electronics 2025, 14(18), 3658; https://doi.org/10.3390/electronics14183658 - 16 Sep 2025

Cited by 1 | Viewed by 2203

Vibrotactile feedback synchronized with audio through haptic music players (HMPs) creates a synergistic effect that has been shown to improve the music listening experience. However, current HMPs are still unable to efficiently retrieve multiple music features, decelerating app scalability and jeopardizing long-term user engagement. This study introduces a wearable HMP that utilizes piezoelectric actuators and a novel audio-tactile rendering algorithm that uses YIN to extract pitch and spectral flux for rhythm. Building upon prior work, the system additionally features a modified discretization step and software optimization to improve multi-feature extraction and tactile display of music. The pitch, melody/timbre, and rhythm displays, respectively, were validated using Mean Average Error (MAE), Dynamic Time Warping (DTW) distance, and accuracy, yielding normalized averages of MAE = 0.1020 and DTW = 0.1518, and a rhythmic pattern accuracy of 97.56%. The Yin algorithm was shown to greatly improve the tactile display of vocals, with slight improvements for bass and accompaniments, while spectral flux and software optimizations significantly improved rhythm display. The wearable HMP effectively communicates multiple music features without the pitfalls of prior approaches. Future research can improve the system’s audio-tactile signal fidelity and explore the qualitative merits of multi-feature extraction in HMPs. Full article

(This article belongs to the Special Issue Intelligent Computing and System Integration)

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11 pages, 3250 KB

Open AccessArticle

A Deep Attention Model for Environmental Sound Classification from Multi-Feature Data

by Jinming Guo, Chuankun Li, Zepeng Sun, Jian Li and Pan Wang

Appl. Sci. 2022, 12(12), 5988; https://doi.org/10.3390/app12125988 - 12 Jun 2022

Cited by 17 | Viewed by 6665

Abstract

Automated environmental sound recognition has clear engineering benefits; it allows audio to be sorted, curated, and searched. Unlike music and language, environmental sound is loaded with noise and lacks the rhythm and melody of music or the semantic sequence of language, making it difficult to find common features representative enough of various environmental sound signals. To improve the accuracy of environmental sound recognition, this paper proposes a recognition method based on multi-feature parameters and time–frequency attention module. It begins with a pretreatment that relies on multi-feature parameters to extract the sound, which supplements the phase information lost by the Log-Mel spectrogram in the current mainstream methods, and enhances the expressive ability of input features. A time–frequency attention module with multiple convolutions is designed to extract the attention weight of the input feature spectrogram and reduce the interference coming from the background noise and irrelevant frequency bands in the audio. Comparative experiments were conducted on three general datasets: environmental sound classification datasets (ESC-10, ESC-50) and an UrbanSound8K dataset. Experiments demonstrated that the proposed method performs better. Full article

(This article belongs to the Special Issue Computer Vision and Pattern Recognition Based on Deep Learning)

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17 pages, 610 KB

Open AccessReview

Singing Voice Detection: A Survey

by Ramy Monir, Daniel Kostrzewa and Dariusz Mrozek

Entropy 2022, 24(1), 114; https://doi.org/10.3390/e24010114 - 12 Jan 2022

Cited by 20 | Viewed by 8133

Abstract

Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melody extraction, and many more. This paper presents a survey on the techniques of singing voice detection with a deep focus on state-of-the-art algorithms such as convolutional LSTM and GRU-RNN. It illustrates a comparison between existing methods for singing voice detection, mainly based on the Jamendo and RWC datasets. Long-term recurrent convolutional networks have reached impressive results on public datasets. The main goal of the present paper is to investigate both classical and state-of-the-art approaches to singing voice detection. Full article

(This article belongs to the Special Issue Methods in Artificial Intelligence and Information Processing)

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19 pages, 4204 KB

Open AccessArticle

Singing Transcription from Polyphonic Music Using Melody Contour Filtering

by Zhuang He and Yin Feng

Appl. Sci. 2021, 11(13), 5913; https://doi.org/10.3390/app11135913 - 25 Jun 2021

Cited by 3 | Viewed by 4558

Abstract

Automatic singing transcription and analysis from polyphonic music records are essential in a number of indexing techniques for computational auditory scenes. To obtain a note-level sequence in this work, we divide the singing transcription task into two subtasks: melody extraction and note transcription. We construct a salience function in terms of harmonic and rhythmic similarity and a measurement of spectral balance. Central to our proposed method is the measurement of melody contours, which are calculated using edge searching based on their continuity properties. We calculate the mean contour salience by separating melody analysis from the adjacent breakpoint connective strength matrix, and we select the final melody contour to determine MIDI notes. This unique method, combining audio signals with image edge analysis, provides a more interpretable analysis platform for continuous singing signals. Experimental analysis using Music Information Retrieval Evaluation Exchange (MIREX) datasets shows that our technique achieves promising results both for audio melody extraction and polyphonic singing transcription. Full article

(This article belongs to the Special Issue Advances in Computer Music)

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