Music Recordings Research Articles

Ensemble width estimation in HRTF-convolved binaural music recordings using an auditory model and a gradient-boosted decision trees regressor

Binaural audio recordings become increasingly popular in multimedia repositories, posing new challenges in indexing, searching, and retrieval of such excerpts in terms of their spatial audio scene characteristics. This paper presents a new method for the automatic estimation of one of the most important spatial attributes of binaural recordings of music, namely “ensemble width.” The method has been developed using a repository of 23,040 binaural excerpts synthesized by convolving 192 multi-track music recordings with 30 sets of head-related transfer functions (HRTF). The synthesized excerpts represented various spatial distributions of music sound sources along a frontal semicircle in the horizontal plane. A binaural auditory model was exploited to derive the standard binaural cues from the synthesized excerpts, yielding a dataset representing interaural level and time differences, complemented by interaural cross-correlation coefficients. Subsequently, a regression method, based on gradient-boosted decision trees, was applied to the formerly calculated dataset to estimate ensemble width values. According to the obtained results, the mean absolute error of the ensemble width estimation averaged across experimental conditions amounts to 6.63° (SD 0.12°). The accuracy of the method is the highest for the recordings with ensembles narrower than 30°, yielding the mean absolute error ranging between 0.8° and 10.2°. The performance of the proposed algorithm is relatively uniform regardless of the horizontal position of an ensemble. However, its accuracy deteriorates for wider ensembles, with the error reaching 25.2° for the music ensembles spanning 90°. The developed method exhibits satisfactory generalization properties when evaluated both under music-independent and HRTF-independent conditions. The proposed method outperforms the technique based on “spatiograms” recently introduced in the literature.

Generating a virtual acoustic environment to approximate the soundscape of a historical cathedral

This paper focuses on the generation of a virtual acoustic environment of a historical cathedral basilica located in St. Augustine, Florida, USA. The virtual acoustic environment is an effective means to study a cultural heritage's soundscape that restricts visitors from accessing and being quiet. The author used a laser scanner to capture the reality of the cathedral's interior, in order to maximize the visual perception of the worship space, and created a 3D digital textured mesh model using the points cloud generated from laser scanning. The spatial experience recorded by a 360 camera with an ambisonic microphone provided the virtual soundwalk at multiple locations. The background and foreground sound were synthesized in Unity using the same music recordings that the church used during the day. The sound pressure levels were closely matched with the ones measured by a sound level meter. The virtual acoustic environment was simulated by using spatial applications in Unity and optimized through the comparison of the room acoustical measurements performed based on ISO 3382.

Separation of music signal spectrograms using copula models

The focus of this paper is on utilizing a copula-based technique for separating music signals. In this method, copulas are utilized to model the dependency structure of the source components. The objective is to minimize the Kullback-Leibler divergence between the copula density of the estimated source components and the copula density of the source components, which is assumed to be unknown. We give an application for the separation of stereo music recording using different copula models. We show through this application that by modeling the dependency between music signals, we can gain insights into the underlying structure of music signals and also we can ameliorate the separation performance.

Framing intimacy: noise as a signifier of intimate silence

ABSTRACT Noise is often conceived of as the opposite of silence. Yet, in the right context, certain kinds of noise can contribute to the perception or interpretation of silence and quietness. Descriptions of this occurrence can be found in music recording discourse. Here, in certain contexts, noises generated by audio equipment, such as hiss, can sometimes be heard to signify silence. This, in turn, can elicit feelings of musical intimacy, which opposes conventional understandings of noise as creating a sense of distance between recorded music and its listeners. This article examines how noise might signify silence and elicit musical intimacy in the context of music recordings. We discuss conceptions of noise and silence before considering how understandings of noise in music recordings have been shaped by events in the history of record production. As a case study, we then turn to Mark Hollis’s eponymous solo album, with its persistent background noise. We analyse the album and surrounding discourse from hermeneutic and semiotic perspectives to investigate how noise can be understood both as silent and musically intimate.

Radical Interpretations of Iconic works for Percussion

The artistic development project Radical Interpretations investigates two iconic works for solo percussion and re-composes these. The goal of the project was to develop new creative and transdisciplinary research in interpretation of musical works. Participants: percussionist Kjell Tore Innervik, Norwegian Academy of Music (NMH), composer Ivar Frounberg, NMH, designer Maziar Raein, Oslo National Academy of the Arts, experience designer Ståle Stenslie, The Oslo School of Architecture and Design, and music recording producer Morten Lindberg. During the 3 years project, the team engaged with the music of Morton Feldman and Iannis Xenakis. The solo percussion pieces The King of Denmark and Psappha were the point of departure. The cd [UTOPIAS ](http://www.2l.no/pages/album/141.html)(2L) contains the pure audio version of the pieces in high definition and immersive sound.-><- On this site you will find other interpretations and iterations of the music made by the team.

Node attribute analysis for cultural data analytics: a case study on Italian XX–XXI century music

Cultural data analytics aims to use analytic methods to explore cultural expressions—for instance art, literature, dance, music. The common thing between cultural expressions is that they have multiple qualitatively different facets that interact with each other in non trivial and non learnable ways. To support this observation, we use the Italian music record industry from 1902 to 2024 as a case study. In this scenario, a possible research objective could be to discuss the relationships between different music genres as they are performed by different bands. Estimating genre similarity by counting the number of records each band published performing a given genre is not enough, because it assumes bands operate independently from each other. In reality, bands share members and have complex relationships. These relationships cannot be automatically learned, both because we miss the data behind their creation, but also because they are established in a serendipitous way between artists, without following consistent patterns. However, we can be map them in a complex network. We can then use the counts of band records with a given genre as a node attribute in a band network. In this paper we show how recently developed techniques for node attribute analysis are a natural choice to analyze such attributes. Alternative network analysis techniques focus on analyzing nodes, rather than node attributes, ending up either being inapplicable in this scenario, or requiring the creation of more complex n-partite high order structures that can result less intuitive. By using node attribute analysis techniques, we show that we are able to describe which music genres concentrate or spread out in this network, which time periods show a balance of exploration-versus-exploitation, which Italian regions correlate more with which music genres, and a new approach to classify clusters of coherent music genres or eras of activity by the distance on this network between genres or years.

Fast audio signal denoising with low-rank approximation using windowed singular value decomposition of frequency division

The method of constructing Hankel matrices and using low-rank approximation has been demonstrated to be an effective approach to audio denoising. However, the significant computational complexity and the trade-off between the denoising quality and the loss of effective signal remain open issues. This paper proposes a denoising method based on frequency-divided Windowed Singular Value Decomposition (WSVD) and exploits the low-rank characteristics and frequency features commonly found in audio signals including speech and music recordings. The method incorporates an improved Lanczos Bidiagonalization algorithm to accelerate the singular value decomposition with low error and high tolerance. Furthermore, techniques are added at the window junctions to maintain the continuity and smoothness of the final audio, thus achieving denoising efficiently and effectively. This paper also assesses the influence of window segmentation length, main frequency domain characteristics, rank selection of Hankel matrix and characteristics of different noises on the final denoising effect. Finally, the denoising algorithm's robustness and effectiveness are validated through simulations and experiments.

Improvised Variations of Sami Al-Shawa

This study aims to detect the most improvised performing method in Sami Al-Shawwa’s works through taking some samples from his musical recordings and highlighting the hardihood creative aspects in those samples, in a scientific thoughtful manner, while working on referencing those methods to special symbols. In fact, those familiar with Sami Al-Shawwa’s recordings may notices that they are full of diverse aesthetics and additions that show his to the moment creative abilities, and emphasize the need to pay more attention to this state of the art musical heritage and use it in a new musical inherent creative models. The results of this study included knowledge of the modalities of some improvised varieties at Sami Al-Shawwa’s performance that form a strong motivation for more deep and studious search discusses the characteristic of the Arabic Music performance and its integration with the contemporary music through high-level scientific methods. Keywords: Musical variations, improvisation, Sami Shawa, violin

Non-intrusive method for audio quality assessment of lossy-compressed music recordings using convolutional neural networks

Most of the existing algorithms for the objective audio quality assessment are intrusive, as they require access both to an unimpaired reference recording and an evaluated signal. This feature excludes them from many practical applications. In this paper, we introduce a non-intrusive audio quality assessment method. The proposed method is intended to account for audio artefacts arising from the lossy compression of music signals. During its development, 250 high-quality uncompressed music recordings were collated. They were subsequently processed using the selection of five popular audio codecs, resulting in the repository of 13,000 audio excerpts representing various levels of audio quality. The proposed non-intrusive method was trained with the data obtained employing a well-established intrusive model (ViSQOL v3). Next, the performance of the trained model was evaluated utilizing the quality scores obtained in the subjective listening tests undertaken remotely over the Internet. The listening tests were carried out in compliance with the MUSHRA recommendation (ITU-R BS.1534-3). In this study, the following three convolutional neural networks were compared: (1) a model employing 1D convolutional filters, (2) an Inception-based model, and (3) a VGG-based model. The last-mentioned model outperformed the model employing 1D convolutional filters in terms of predicting the scores from the listening tests, reaching a correlation value of 0.893. The performance of the Inceptionbased model was similar to that of the VGG-based model. Moreover, the VGG-based model outperformed the method employing a stacked gated-recurrent-unit-based deep learning framework, recently introduced by Mumtaz et al. (2022).

Exploring music listening patterns: an online survey

An online survey was carried out to explore how respondents listen to music recordings. It was anticipated that the listener’s preferences would be influenced by various factors, such as age, music genre, the contexts in which they listen, and their favored methods of music consumption. Consequently, the data were collected to analyze these relationships. The survey, structured as a web application, encompassed 23 questions, with seven specifically aimed at defining the respondents and the remainder contributing to the dataset for analysis. The results reveal a prevailing preference for listening to music via streaming platforms. Respondents predominantly engage in passive listening, where music becomes a background presence without commanding their focused attention. Moreover, the data also highlight a noteworthy correlation between preferred music genres and the age of the listeners.

Feature Extraction in Music information retrival using Machine Learning Algorithms

Music classification is essential for faster Music record recovery. Separating the ideal arrangement of highlights and selecting the best investigation technique are critical for obtaining the best results from sound grouping. The extraction of sound elements could be viewed as an exceptional case of information sound information being transformed into sound instances. Music division and order can provide a rich dataset for the analysis of sight and sound substances. Because of the great dimensionality of sound highlights as well as the variable length of sound fragments, Music layout is dependent on the overpowering computation. By focusing on rhythmic aspects of different songs, this article provides an introduction of some of the possibilities for computing music similarity. Almost every MIR toolkit includes a method for extracting the beats per minute (BPM) and consequently the tempo of each music. The simplest method of computing very low-level rhythmic similarities is to sort and compare songs solely by their tempo There are undoubtedly far better and more precise solutions. work discusses some of the most promising ways for computing rhythm similarities in a Big Data framework using machine Learning algorithms.

Continuous Hidden Markov Algorithm-Based Multimedia Melody Retrieval System for Music Education

Education professionals receive instruction in Music Education (ME) to prepare for prospective jobs like secondary or primary music teachers, schools ensembles executives, or ensembles directors at music institutions. In the discipline of music education, educators do original research on different approaches to teaching and studying music. The most accurate and effective method of extracting music from huge music databases has become one of the most frequently discussed participants in contemporary multimedia information retrieval development. The essence of multimedia material is presented within a range of techniques since it is not bound to a single side. These several categories could consist of the song’s audio components and lyrics for musical information. Retrieving melodic information, subsequently, becomes the main focus of most recent studies. Aside from being an expensive deviate from academics, music programs are neither a viable profession neither a valid pastime. Therefore, in this study, we offer a Continuous Hidden Markov Algorithm (CHMA) related a novel method for recovering melodies from musical multimedia recordings. CHMA is considered to be the most basic dynamic Bayesian network. Two various types of audio frame features and audio example features are extracted throughout the feature extraction procedure from the audio signal according to unit length. Every music clip receives a unique approach that we implement with concurrently using various CHMA. The initial music gets processed using a trained CHMA that monitors fundamental frequencies, maps states, and generates retrieval outcomes. The training time for Traditional opera reached 455.76 minutes, the testing time for Narration achieved 56.10 minutes, and the recognition accuracy for advertisement reached an impressive 98.02%. A subsequently experimental result validates the applicability of the proposed approach.

From Music Scores to Audio Recordings: Deep Pitch-Class Representations for Measuring Tonal Structures

The availability of digital music data in various modalities provides opportunities both for music enjoyment and music research. Regarding the latter, the computer-assisted analysis of tonal structures is a central topic. For Western classical music, studies typically rely on machine-readable scores, which are tedious to create for large-scale works and comprehensive corpora. As an alternative, music audio recordings, which are readily available, can be analyzed with computational methods. With this paper, we want to bridge the gap between score- and audio-based measurements of tonal structures by leveraging the power of deep neural networks. Such networks are commonly trained in an end-to-end fashion, which introduces biases towards the training repertoire or towards specific annotators. To overcome these problems, we propose a multi-step strategy. First, we compute pitch-class representations of the audio recordings using networks trained on score–audio pairs. Second, we measure the presence of specific tonal structures using a pattern-matching technique that solely relies on music theory knowledge and does not require annotated training data. Third, we highlight these measurements with interactive visualizations, thus leaving the interpretation to the musicological experts. Our experiments on Richard Wagner's large-scale cycle Der Ring des Nibelungen indicate that deep pitch-class representations lead to a high similarity between score- and audio-based measurements of tonal structures, thus demonstrating how to leverage multimodal data for application scenarios in the computational humanities, where an explicit and interpretable methodology is essential.

Technical, Musical, and Legal Aspects of an AI-Aided Algorithmic Music Production System

Even though algorithmic composition might be considered a centuries-old concept, it has been gaining particular momentum since the introduction of computer-based techniques. The development of artificial intelligence (AI) methods, culminating in the latest achievements of deep learning techniques, has provided tools to automatically compose and even produce music. This paper discusses various aspects of the entire process within a context of designing a system able to automatically generate a score and recordings belonging to selected musical genres. It begins with the idea and design overview, followed by considerations regarding the algorithmic formulation of selected musical rules and principles. The system implements a hybrid approach, combining conventional, i.e., stochastic or rule-based, and AI elements. The latter are applied to facilitate the generation of selected layers of composition and to constitute a classifier with a task of evaluating the generated recordings. Selected stages of music generation are discussed, for example how motifs are processed into phrases and how phrases are used in the context of a whole song. To validate the system operation results, an evaluation of the quality of the produced music recordings was conducted, including a test with a group of listeners. The analysis also touches upon some legal aspects related to the creation of algorithmic compositions.

‘It's mostly an accompaniment to something’

‘It's mostly an accompaniment to something’

Acoustic Echo Cancellation using Adaptive Filter for Quranic Accent Signals

Quranic recordings andechoed portions of the emphasis are susceptible to signal reverberation, particularly when being listened to in a conference room. Tajweed and Quranic verse rule identification are susceptible to additive noise, which could lower classification accuracy. In order to reflect the most correct rate following pattern categorization, this study suggested the appropriate use of three adaptive algorithms: Affine Projection (AP), Least Mean Square (LMS), and Recursive Least Squares (RLS). For feature extraction, Mel Frequency Cepstral Coefficient is used together with Probabilities Principal Component Analysis (PPCA), K-Neural Network (KNN) and Gaussian Mixture Model (GMM). AP indicates 93.9% for all of the classification algorithm in used, while for LMS and RLS the results are differed varies on different pattern classification algorithm stated whereby with LMS and PPCA classification, 96.9 % for accuracy and 84.8% accuracy for LMS and KNN. While for RLS and GMM, 96.9% was achieved and the results were reduced for both KNN and PPCA. The analysis has resulted for both on accuracies within different filtering algirithm and classification for accuracy and ERLE(dB).Towards this research it is hope will embark more understanding towards echo cancellation and quality of sound recordings that may affected even to the Quranic recordings.KeywordsAdaptive filtering, acoustic echo cancellation, recursive least squares, least mean square, affine projection, accuracy rate1.IntroductionUnusually big peaks have been produced by frequency response, masking, and emerging peaks from sound systems. Noise can be introduced into music recordings from the surrounding environment, during the recording system when audio signals change, or during the indexing process. These noise signals can disrupt and lower the quality and efficiency of the recording. Quranic recordings may also be affected by echoes, which can interfere with the recording process. The speaker, microphone, and transmission path are among the variables that affect.

Is Hey Jude in the right key? Cognitive components of absolute pitch memory.

Most individuals, regardless of formal musical training, have long-term absolute pitch memory (APM) for familiar musical recordings, though with varying levels of accuracy. The present study followed up on recent evidence suggesting an association between singing accuracy and APM (Halpern & Pfordresher, 2022, Attention, Perception, & Psychophysics, 84(1), 260-269), as well as tonal short-term memory (STM) and APM (Van Hedger et al., 2018,Quarterly Journal of Experimental Psychology, 71(4), 879-891). Participants from three research sites (n = 108) completed a battery of tasks including APM, tonal STM, singing accuracy, and self-reported auditory imagery. Both tonal STM and singing accuracy predicted APM, replicating prior results. Tonal STM also predicted singing accuracy, music training, and auditory imagery. Further tests suggested that the association between APM and singing accuracy was fully mediated by tonal STM. This pattern comports well with models of vocal pitch matching that include STM for pitch as a mechanism for sensorimotor translation.

Data augmentation techniques for classification of music genre

Music genre classification, a crucial aspect in the field of audio processing, has been widely investigated using different machine learning methods. However, there is still a need to determine the most optimal application between Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) in the field of music genre classification, and the augmentation and enhancement of the data utilized for these machine learning techniques is still an important topic to study. The objective of this research is to identify the most effective solution and to improve the accuracy of music categorization. This will be achieved through an in-depth comparison of various data augmentation techniques and neural networks. In this study, both CNN and LSTM networks are optimized and trained. The dataset has been enhanced by extracting Mel Frequency Cepstral Coefficients (MFCC) data from music recordings using three different sampling techniques. The thorough approach undertaken facilitates a detailed assessment of the effectiveness of each method in classifying genres. The experimental findings show that the combination of CNN and random sampling outperforms all other tested algorithms, resulting in a significant enhancement in genre classification accuracy. This research provides valuable findings that can inform future studies in the pursuit of more effective techniques for classifying music genres.

Have we sold our souls to the drum machine? A historical analysis of tempo stability in Western music recordings

Musicians have long sought technology to facilitate and develop their abilities to maintain stable tempos. The rise of inherently tempo-stable instruments and performance technologies in the late 20th century—for example, turntables, sequencers, drum machines, click tracks, and post-production quantization—has fed an increasing trend toward tempo stability in Western popular music. However, the exact progression of this historical change has not been systematically documented. In this article, we report a historical analysis of tempo stability in recordings of Western Classical and Anglo-American popular music released between 1920 and 2020, in particular testing the hypothesis that tempo in certain popular genres became significantly more stable in the period 1980–2000. We distinguish between gradual changes of tempo that are not perceived as discontinuities ( tempo drift) and abrupt changes of tempo ( tempo shifts), and seek to quantify both types of tempo instability. Using the Spotify web API, we sampled measure-by-measure tempo estimates from over 45,000 tracks in seven genres: Blues, Classical, Country, Electronic Dance Music (EDM), Hip hop, Jazz, and Rock. Our analyses reveal significant changes in tempo stability in Rock, Country, and Blues music, consistent with our hypothesis. We next propose, compare, and evaluate several approaches to identifying artificially tempo-stable tracks from their tempo estimates, using the opposition between Classical and Hip hop as a faux ground truth. Data, analyses, and interactive visualizations are available for inspection on our website https://ccml.music.gatech.edu/TempoStability .

Classification of Classical Indian Music Tabla Taals using Deep Learning

In the research that we are bringing to light, we profoundly explore the categorization of Classical Indian Music Tabla Taals. This emphasizes widely recognized taals such as Addhatrital, Ektal, Rupak, Dadra, Deepchandi, Jhaptal, Trital, and Bhajani. To push the boundaries of our understanding, we implement a mixed-methods approach tethering both Feedforward Neural Networks (FNN) and Convolutional Neural Networks (CNN). These state-of-the-art technologies enable us to dissect and categorize tabla taals efficiently. In essence, the hallmark of Classical Indian music is its complex and multifaceted rhythms brought to life by the primal percussive instrument - the tabla. The conception and reproduction of these nuanced taals require technical finesse. Thus, accompanying the digital revolution and the eclectic musical preferences, it becomes essential for advanced methodologies to pinpoint and classify tabla taals. The hardcover of our research opens up to the magnificent crafting of an unmatched model employing both FNN and CNN. This blend enables us to recognize diverse features unique to tabla taals like Addhatrital, Ektal, Rupak, Dadra, Deepchandi, Jhaptal, Trital, and Bhajani. The model obtained its bosom knowledge during training from an assortment of Classical Indian music recordings showcasing these invigorating taals. This fosters a broader understanding regarding the array of minute differences brimming within each rhythmic inheritance. To bring user interaction to life, we have embedded a Graphical User Interface (GUI). This empowers users to introduce an audio file filled with table music from the taals listed and receive on-the-spot recognition. refining their connection and knowledge of the taal in question. Our research findings procure paramount importance in the scape of music analysis, especially framed within the heart of Classical Indian Music. We propose a system that would serve as a tool for amateur table players to learn the skill well and master their art. Instructors could also utilize it for training purposes. It opens a new window of possibilities providing an advanced model for intuitive, swift, and accurate automated identification of tabla taals.