Search Results (309)

In the context of typhlo music therapy, personalized interventions can significantly enhance the therapeutic experience for visually impaired children. Leveraging a data-driven approach, we incorporate action-rule discovery to provide insights into the factors of music that may benefit individual children. The system utilizes a comprehensive dataset developed in collaboration with an experienced music therapist, special educator, and clinical psychologist, encompassing meta-decision attributes, decision attributes, and musical features such as tempo, rhythm, and pitch. By extracting and analyzing these features, our methodology identifies key factors that influence therapeutic outcomes. Some themes discovered through action-rule discovery include the effect of harmonic richness and loudness on expression and communication. The main findings demonstrate the system’s ability to offer personalized, impactful, and actionable insights, leading to improved therapeutic experiences for children undergoing typhlo music therapy. Our conclusions highlight the system’s potential to transform music therapy by providing therapists with precise and effective tools to support their patients’ developmental progress. This work shows the significance of integrating advanced data analysis techniques in therapeutic settings, paving the way for future enhancements in personalized music therapy interventions. Full article

Sacred choral music continues to hold a significant place in contemporary concert settings, with historical and newly composed works featuring in today’s choral programmes. Contemporary choral composers have continued to engage with the longstanding tradition of setting sacred texts to music, bringing fresh interpretations through their innovative compositional techniques and fusion of styles. Irish composer Rhona Clarke’s (b. 1958) expansive choral oeuvre includes a wealth of both sacred and secular compositions but reveals a notable propensity for the setting of sacred texts in Latin. Her synthesis of archaic and contemporary techniques within her work demonstrates both the solemn and visceral aspects of these texts, as well as a clear nod to tradition. This article focuses on Clarke’s choral work O Vis Aeternitatis (2020), a setting of a text by the medieval musician and saint Hildegard of Bingen (c. 1150). Through critical score analysis, we investigate the piece’s melodic, harmonic, and textural frameworks; the influence of Hildegard’s original chant; and the use of extended vocal techniques and contrasting vocal timbres as we articulate core characteristics of Clarke’s compositional style and underline her foregrounding of the more visceral aspects of Hildegard’s words. Clarke’s fusion of creative practices from past and present spotlights moments of dramatic escalation and spiritual importance, and exhibits the composer’s distinctive compositional voice as she reimagines Hildegard’s text for the twenty-first century. Full article

In this paper, a set of digital signal processing (DSP) procedures tailored for the analysis of complex musical tones with prominent inharmonicity is presented. These procedures are implemented within a MATLAB-based application and organized into three submodules. The application follows a structured DSP chain: basic signal manipulation; spectral content analysis; estimation of the inharmonicity coefficient and the number of prominent partials; design of a dedicated filter bank; signal decomposition into subchannels; subchannel analysis and envelope extraction; and, finally, recombination of the subchannels into a wideband signal. Each stage in the chain is described in detail, and the overall process is demonstrated through representative examples. The concept and the accompanying application are initially intended for rapid post-processing of recorded signals, offering a tool for enhanced signal annotation. Additionally, the built-in features for subchannel manipulation and recombination enable the preparation of stimuli for perceptual listening tests. The procedures have been tested on a set of recorded tones from various string instruments, including those with pronounced inharmonicity, such as the piano, harp, and harpsichord. Full article

This paper presents a novel cochlear implant (CI) sound coding strategy called Spectral Feature Extraction (SFE). The SFE is a novel Fast Fourier Transform (FFT)-based Continuous Interleaved Sampling (CIS) strategy that provides less-smeared spectral cues to CI patients compared to Crystalis, a predecessor strategy used in Oticon Medical devices. The study also explores how the SFE can be enhanced into a Temporal Fine Structure (TFS)-based strategy named Spectral Event Extraction (SEE), combining spectral sharpness with temporal cues. Background/Objectives: Many CI recipients understand speech in quiet settings but struggle with music and complex environments, increasing cognitive effort. De-smearing the power spectrum and extracting spectral peak features can reduce this load. The SFE targets feature extraction from spectral peaks, while the SEE enhances TFS-based coding by tracking these features across frames. Methods: The SFE strategy extracts spectral peaks and models them with synthetic pure tone spectra characterized by instantaneous frequency, phase, energy, and peak resemblance. This deblurs input peaks by estimating their center frequency. In SEE, synthetic peaks are tracked across frames to yield reliable temporal cues (e.g., zero-crossings) aligned with stimulation pulses. Strategy characteristics are analyzed using electrodograms. Results: A flexible Frequency Allocation Map (FAM) can be applied to both SFE and SEE strategies without being limited by FFT bandwidth constraints. Electrodograms of Crystalis and SFE strategies showed that SFE reduces spectral blurring and provides detailed temporal information of harmonics in speech and music. Conclusions: SFE and SEE are expected to enhance speech understanding, lower listening effort, and improve temporal feature coding. These strategies could benefit CI users, especially in challenging acoustic environments. Full article

Cognitive impairment is a prevalent and disabling feature of multiple sclerosis (MS), significantly impacting patients’ quality of life (QoL) and psychological well-being. Despite its clinical relevance, there are currently no approved pharmacological treatments for cognitive deficits in MS, highlighting the need for effective non-pharmacological interventions. This narrative review explores evidence from studies evaluating the efficacy of cognitive re-education (CR) approaches (including traditional, group-based, computer-assisted, virtual reality, and innovative methods such as music therapy) on cognitive and QoL outcomes in people with MS. The findings demonstrate that while CR consistently influences cognitive domains such as memory, attention, and executive function, its effects on QoL are more variable and often depend on intervention type, duration, and individual patient characteristics. Notably, integrative approaches like virtual reality and music therapy show promising results in enhancing both cognitive performance and psychosocial well-being. Several studies report that cognitive gains are accompanied by improvements in mental health and functional QoL, particularly when interventions are tailored to individual needs and delivered within multidisciplinary frameworks. However, some interventions yield only limited or transient QoL benefits, underlining the importance of personalized, goal-oriented strategies that address both cognitive and psychosocial dimensions. Further research is needed to optimize intervention strategies and clarify the mechanisms linking cognitive and QoL outcomes. Full article

Chinese traditional instruments are diverse and encompass a rich variety of timbres and rhythms, presenting considerable research potential. This work proposed a deep-learning framework for the automated classification of Chinese traditional instruments, addressing the challenges of acoustic diversity and cultural preservation. By integrating two datasets, CTIS and ChMusic, we constructed a combined dataset comprising four instrument families: wind, percussion, plucked string, and bowed string. Three time-frequency features, namely MFCC, CQT, and Chroma, were extracted to capture diverse sound information. A convolutional neural network architecture was designed, incorporating 3-channel spectrogram feature stacking and a hybrid channel–spatial attention mechanism to enhance the extraction of critical frequency bands and feature weights. Experimental results demonstrated that the feature-fusion method improved classification performance compared to a single feature as input. Meanwhile, the attention mechanism further boosted test accuracy to 98.79%, outperforming baseline models by 2.8% and achieving superior F1 scores and recall compared to classical architectures. Ablation study confirmed the contribution of attention mechanisms. This work validates the efficacy of deep learning in preserving intangible cultural heritage through precise analysis, offering a feasible methodology for the classification of Chinese traditional instruments. Full article

In music, plagiarism has been an important but troubled issue, which becomes ever more critical with the widespread usage of generative AI tools. Meanwhile, the development of techniques for music similarity detection has been hampered by the scarcity of legally verified data on plagiarism. In this paper, we present a technical solution for training music similarity detection models through the use of comparative imagery data. With the aid of feature-based analysis and data visualization, we conducted experiments to analyze how different music features may contribute to the judgment of plagiarism. While the feature-based analysis guided us to focus on a subset of features, whose similarity is typically associated with music plagiarism, data visualization inspired us to train machine learning models using such comparative imagery instead of using audio signals directly. We trained feature-based sub-models (convolutional neural networks) using imagery data and an ensemble model with Bayesian interpretation for combining the predictions of the sub-models. We tested the trained model with legally verified data as well as AI-generated music, confirming that the models produced with our approach can detect similarity patterns which are typically associated with music plagiarism. Furthermore, using imagery data as the input and output of an ML model has been proven to facilitate explainable AI. Full article

Instrument recognition is a crucial aspect of music information retrieval, and in recent years, machine learning-based methods have become the primary approach to addressing this challenge. However, existing models often struggle to accurately identify multiple instruments within music tracks that vary in length and quality. One key issue is that the instruments of interest may not appear in every clip of the audio sample, and when they do, they are often unevenly distributed across different sections of the track. Additionally, in polyphonic music, multiple instruments are often played simultaneously, leading to signal overlap. Using the same overlapping audio signals as partial classification features for different instruments will reduce the distinguishability of features between instruments, thereby affecting the performance of instrument recognition. These complexities present significant challenges for current instrument recognition models. Therefore, this paper proposes a multi-instance multi-scale graph attention neural network (MMGAT) with label semantic embeddings for instrument recognition. MMGAT designs an instance correlation graph to model the presence and quantitative timbre similarity of instruments at different positions from the perspective of multi-instance learning. Then, to enhance the distinguishability of signals after the overlap of different instruments and improve classification accuracy, MMGAT learns semantic information from the labels of different instruments as embeddings and incorporates them into the overlapping audio signal features, thereby enhancing the differentiability of audio features for various instruments. MMGAT then designs an instance-based multi-instance multi-scale graph attention neural network to recognize different instruments based on the instance correlation graphs and label semantic embeddings. The effectiveness of MMGAT is validated through experiments and compared to commonly used instrument recognition models. The experimental results demonstrate that MMGAT outperforms existing approaches in instrument recognition tasks. Full article

High-precision Direction-of-Arrival (DOA) estimation leveraging multi-sensor array architectures represents a frontier research domain in advanced array signal processing systems. Compared to traditional model-driven estimation methods like MUSIC and ESPRIT, data-driven approaches offer advantages such as higher estimation accuracy and simpler structures. Convolutional neural networks (CNNs) currently dominate deep learning approaches for DOA estimation. However, traditional CNNs suffer from limitations in capturing global features of covariance matrices due to their restricted local receptive fields, alongside challenges such as noise sensitivity and poor interpretability. To address these issues, we propose a novel Discrete Fourier Transform (DFT)-based deep learning framework for DOA estimation called DFNeT, leveraging the advantages of Fourier transform-enhanced networks in global modeling, computational efficiency, and noise robustness. Specifically, our approach introduces a DFT-based deep feature fusion network to denoise covariance matrices by integrating spatial and frequency-domain information. Subsequently, a series of DFT modules are designed to extract discriminative frequency-domain features, enabling accurate and robust DOA estimation. This method effectively mitigates noise interference while enhancing the interpretability of feature extraction through explicit frequency-domain operations. The simulation results demonstrate the effectiveness of the proposed method. Full article

In deep learning, Long Short-Term Memory (LSTM) is a well-established and widely used approach for music generation. Nevertheless, creating musical compositions that match the quality of those created by human composers remains a formidable challenge. The intricate nature of musical components, including pitch, intensity, rhythm, notes, chords, and more, necessitates the extraction of these elements from extensive datasets, making the preliminary work arduous. To address this, we employed various tools to deconstruct the musical structure, conduct step-by-step learning, and then reconstruct it. This article primarily presents the techniques for dissecting musical components in the preliminary phase. Subsequently, it introduces the use of LSTM to build a deep learning network architecture, enabling the learning of musical features and temporal coherence. Finally, through in-depth analysis and comparative studies, this paper validates the efficacy of the proposed research methodology, demonstrating its ability to capture musical coherence and generate compositions with similar styles. Full article

Deciphering the structural hierarchy of musical compositions is indispensable for a range of music analysis applications, encompassing feature extraction, data compression, interpretation, and visualization. In this paper, we introduce a quantitative model grounded in fractal theory to evaluate the significance of individual notes within a musical piece. To analyze the quantized note importance, we adopt a rhythm-based approach and propose a series of detection operators informed by fundamental rhythmic combinations. Employing the Mamba model, we carry out recursive detection operations that offer a hierarchic understanding of musical structures. By organizing the composition into a tree data structure, we achieve an ordered layer traversal that highlights the music piece’s multi-dimensional features. Musical compositions often exhibit intrinsic symmetry in their temporal organization, manifested through repetition, variation, and self-similar patterns across scales. Among these symmetry properties, fractality stands out as a prominent characteristic, reflecting recursive structures both rhythmically and melodically. Our model effectively captures this property, providing insights into the fractal-like regularities within music. It also proves effective in musical phrase boundary detection tasks, enhancing the clarity and visualization of musical information. The findings illustrate the model’s potential to advance the quantitative analysis of music hierarchy, promoting novel methodologies in musicological research. Full article

This paper introduces Harmonizer, a universal framework designed for tokenizing heterogeneous input signals, including text, audio, and video, to enable seamless integration into multimodal large language models (LLMs). Harmonizer employs a unified approach to convert diverse, non-linguistic signals into discrete tokens via its FusionQuantizer architecture, built on FluxFormer, to efficiently capture essential signal features while minimizing complexity. We enhance features through STFT-based spectral decomposition, Hilbert transform analytic signal extraction, and SCLAHE spectrogram contrast optimization, and train using a composite loss function to produce reliable embeddings and construct a robust vector vocabulary. Experimental validation on music datasets such as E-GMD v1.0.0, Maestro v3.0.0, and GTZAN demonstrates high fidelity across 288 s of vocal signals (MSE = 0.0037, CC = 0.9282, Cosine Sim. = 0.9278, DTW = 12.12, MFCC Sim. = 0.9997, Spectral Conv. = 0.2485). Preliminary tests on text reconstruction and UCF-101 video clips further confirm Harmonizer’s applicability across discrete and spatiotemporal modalities. Rooted in the universality of wave phenomena and Fourier theory, Harmonizer offers a physics-inspired, modality-agnostic fusion mechanism via wave superposition and interference principles. In summary, Harmonizer integrates natural language processing and signal processing into a coherent tokenization paradigm for efficient, interpretable multimodal learning. Full article

Automatic music transcription in multi-instrument settings remains a highly challenging task due to overlapping harmonics and diverse timbres. To address this, we propose the Periodicity–Frequency Fusion Network (PF2N), a lightweight and modular component that enhances transcription performance by integrating both spectral and periodicity-domain representations. Inspired by traditional combined frequency and periodicity (CFP) methods, the PF2N reformulates CFP as a neural module that jointly learns harmonically correlated features across the frequency and cepstral domains. Unlike handcrafted alignments in classical approaches, the PF2N performs data-driven fusion using a learnable joint feature extractor. Extensive experiments on three benchmark datasets (Slakh2100, MusicNet, and MAESTRO) demonstrate that the PF2N consistently improves transcription accuracy when incorporated into state-of-the-art models. The results confirm the effectiveness and adaptability of the PF2N, highlighting its potential as a general-purpose enhancement for multi-instrument AMT systems. Full article

To tackle the challenges of relative attitude adaptability and limited sample availability in underwater moving target recognition for active sonar, this study focuses on key aspects such as feature extraction, network model design, and information fusion. A pseudo-three-dimensional spatial feature extraction method is proposed by integrating generalized MUSIC with range–dimension information. The pseudo-WVD time–frequency feature is enhanced through the incorporation of prior knowledge. Additionally, the Doppler frequency shift distribution feature for underwater moving targets is derived and extracted. A multidimensional feature information fusion network model based on meta-learning is developed. Meta-knowledge is extracted separately from spatial, time–frequency, and Doppler feature spectra, to improve the generalization capability of single-feature task networks during small-sample training. Multidimensional feature information fusion is achieved via a feature fusion classifier. Finally, a sample library is constructed using simulation-enhanced data and experimental data for network training and testing. The results demonstrate that, in the few-sample scenario, the proposed method leverages the complementary nature of multidimensional features, effectively addressing the challenge of limited adaptability to relative horizontal orientation angles in target recognition, and achieving a recognition accuracy of up to 97.1%. Full article

This paper integrates concepts from neurobiology, marketing and musicology to propose a causal model of music appreciation and exploratory listening, using directed acyclic graphs (DAGs) and structural equation models (SEMs). The key concepts are music appreciation (measured on a scale from 1 to 10), evaluations, experiences and the qualities of musical features, which the listeners explore and describe from a first-person perspective. The qualities are understood in terms of a satisfaction or dissatisfaction rating of operational features. The development of the causal model is based on a reiterative methodology involving surveys. Applying the causal model to a large survey of 800 listeners reveals that listeners adopt a slightly different causal pathway for their appreciation of liked versus disliked music. When listeners dislike music, the source of their dissatisfaction is more consistently attributed to the perceived or missed musical qualities rather than to their personal experiences. The iterative methodology and causal modeling offer a foundation for further investigation and refinement in various listening contexts. Full article