Music Tracks Research Articles

7-6 映像コンテンツ解析によるBGMサウンドトラックの自動生成(第7部門 映像コンテンツ)

7-6 映像コンテンツ解析によるBGMサウンドトラックの自動生成(第7部門 映像コンテンツ)

The Effects of Diegetic and Nondiegetic Music on Viewers’ Interpretations of a Film Scene

Previous studies have shown that pairing a film excerpt with different musical soundtracks can change the audience’s interpretation of the scene. This study examined the effects of mixing the same piece of music at different levels of loudness in a film soundtrack to suggest diegeticmusic (“source music,” presented as if arising from within the fictional world of the film characters) or to suggest nondiegetic music (a “dramatic score” accompanying the scene but not originating from within the fictional world). Adjusting the level of loudness significantly altered viewers’ perceptions of many elements that are fundamental to the storyline, including inferences about the relationship, intentions, and emotions of the film characters, their romantic interest toward each other, and the overall perceived tension of the scene. Surprisingly, varying the loudness (and resulting timbre) of the same piece of music produced greater differences in viewers’ interpretations of the film scene and characters than switching to a different music track. This finding is of theoretical and practical interest as changes in loudness and timbre are among the primary post-production modifications sound editors make to differentiate “source music” from “dramatic score” in motion pictures, and the effects on viewers have rarely been empirically investigated.

Automatic music genre classification based on musical instrument track separation

The aim of this article is to investigate whether separating music tracks at the pre-processing phase and extending feature vector by parameters related to the specific musical instruments that are characteristic for the given musical genre allow for efficient automatic musical genre classification in case of database containing thousands of music excerpts and a dozen of genres. Results of extensive experiments show that the approach proposed for music genre classification is promising. Overall, conglomerating parameters derived from both an original audio and a mixture of separated tracks improve classification effectiveness measures, demonstrating that the proposed feature vector and the Support Vector Machine (SVM) with Co-training mechanism are applicable to a large dataset.

Virtual Music Teacher For Monophonic Music Signals

The target of this thesis paper is to develop digital signal processing algorithms and implement a software environment to detect the pitch of music signals. Analysis of music signals, detecting the pitch and tracking notes is the main part of the work. Different state of the art pitch detection algorithms were investigated and their advantages and disadvantages were studied and compared among each other. In this thesis, the Wavelet transform method is used for monophonic pitch detection. The proposed method was developed using MATLAB® and tested for various music tracks which were produced from multi-track MIDI and audio editing software and some downloaded from the net. MATLAB® graphical user interface is used to display the detected as a feedback to the musician.

Combining machine-based and econometrics methods for policy analytics insights

Computational social science supports study of e-commerce policy analytics issues.Advances have been made for the discovery of new policy-related insights.Areas covered are: business, consumer and social public and private settings.Machine-based methods are combined with explanatory econometrics and statistics.Empirical illustrations are in four contemporary e-commerce research issue areas.Mobile phone stock trading, music popularity, TV viewing, video-on-demand services. Computational Social Science (CSS) has become a mainstream approach in the empirical study of policy analytics issues in various domains of e-commerce research. This article is intended to represent recent advances that have been made for the discovery of new policy-related insights in business, consumer and social settings. The approach discussed is fusion analytics, which combines machine-based methods from Computer Science (CS) and explanatory empiricism involving advanced Econometrics and Statistics. It explores several efforts to conduct research inquiry in different functional areas of Electronic Commerce and Information Systems (IS), with applications that represent different functional areas of business, as well as individual consumer, social and public issues. Recent developments and shifts in the scientific study of technology-related phenomena and Social Science issues in the presence of historically-large datasets prompt new forms of research inquiry. They include blended approaches to research methodology, and more interest in the production of research results that have direct application to industry contexts. This article showcases the methods shifts and several contemporary applications. They discuss: (1) feedback effects in mobile phone-based stock trading; (2) sustainability of top-rank chart popularity of music tracks; (3) household TV viewing patterns; and (4) household sampling and purchases of video-on-demand (VoD) services. The range of applicability of the ideas goes beyond the scope of these illustrations, to include issues in public services, healthcare, product and service deployment, public opinion and elections, electronic auctions, and travel and tourism services. In fact, the coverage is as broad as for-profit and for-non-profit, private and public, and governmental and non-governmental institutions.

The Digital Music Lab

In musicology and music research generally, the increasing availability of digital music, storage capacities, and computing power enable and require new and intelligent systems. In the transition from traditional to digital musicology, many techniques and tools have been developed for the analysis of individual pieces of music, but large-scale music data that are increasingly becoming available require research methods and systems that work on the collection-level and at scale. Although many relevant algorithms have been developed during the past 15 years of research in Music Information Retrieval, an integrated system that supports large-scale digital musicology research has so far been lacking. In the Digital Music Lab (DML) project, a collaboration among music librarians, musicologists, computer scientists, and human-computer interface specialists, the DML software system has been developed that fills this gap by providing intelligent large-scale music analysis with a user-friendly interactive interface supporting musicologists in their exploration and enquiry. The DML system empowers musicologists by addressing several challenges: distributed processing of audio and other music data, management of the data analysis process and results, remote analysis of data under copyright, logical inference on the extracted information and metadata, and visual web-based interfaces for exploring and querying the music collections. The DML system is scalable and based on Semantic Web technology and integrates into Linked Data with the vision of a distributed system that enables music research across archives, libraries, and other providers of music data. A first DML system prototype has been set up in collaboration with the British Library and I Like Music Ltd. This system has been used to analyse a diverse corpus of currently 250,000 music tracks. In this article, we describe the DML system requirements, design, architecture, components, and available data sources, explaining their interaction. We report use cases and applications with initial evaluations of the proposed system.

“We Owe a Historical Debt to No One”

A collection of photographs by colonial officer and amateur anthropologist James Henry Green now in the collections of Brighton Museum & Art Gallery has been extensively reappropriated and reused by members of the Kachin community from northern Burma who originally formed their subject. This article considers one specific use, in a music track and video produced as part of a collaboration between a Burma-based Kachin rap artist and a Kachin singer and media producer currently living in China. Released at a critical moment for the Kachin community, following the recent breakdown of a long-standing cease-fire agreement between the Kachin Independence Organization and the Burmese government, the track and video reveal some of the tensions at play between the outward-looking, transnational, and cosmopolitan tendencies of the growing overseas Kachin community and the nostalgic, territorially based ethnonationalism that has been at the heart of Kachin demands for greater political autonomy since the 1960s.

Virtual Music Teacher For Monophonic Music Signals

The target of this thesis paper is to develop digital signal processing algorithms and implement a software environment to detect the pitch of music signals. Analysis of music signals, detecting the pitch and tracking notes is the main part of the work. Different state of the art pitch detection algorithms were investigated and their advantages and disadvantages were studied and compared among each other. In this thesis, the Wavelet transform method is used for monophonic pitch detection. The proposed method was developed using MATLAB® and tested for various music tracks which were produced from multi-track MIDI and audio editing software and some downloaded from the net. MATLAB® graphical user interface is used to display the detected as a feedback to the musician.

Getting into the Groove: The Development of Tempo‐Flexibility Between 10 and 18 Months of Age

Despite good early rhythm processing abilities, and clear enjoyment of music, infants appear not to be able to spontaneously synchronize their movement to the beat of a song (Zentner and Eerola, Proceedings of the National Academy of Sciences, 107, 2010, 5768). We present a new social bell‐ringing task designed to facilitate synchronous movement to music in infants. Ten‐month‐olds, 18‐month‐olds, and adults were played musical tracks of various tempos and given handheld bells to ring, in the presence of either a live experimenter or an animated nonsocial stimulus. Surface electromyography (EMG) was used to measure the timing of arm movements during periods of bell ringing. Infants showed no evidence of synchronous bell ringing at any tempo. However, while the 10‐month‐olds did not modulate their ringing to the music tempo, the 18‐month‐olds showed tempo‐flexibility. Moreover, 18‐month‐olds displayed more associated behaviors such as bouncing and rocking in the absence (rather than presence) of a social partner, whereas the behavior of the 10‐month‐olds was not modulated by the presence or absence of a social partner. The results suggest a distinction between “moving together” and “moving to the beat,” which may have separate underlying mechanisms and developmental trajectories.

Human emotion recognition and analysis in response to audio music using brain signals

Human emotion recognition using brain signals is an active research topic in the field of affective computing. Music is considered as a powerful tool for arousing emotions in human beings. This study recognized happy, sad, love and anger emotions in response to audio music tracks from electronic, rap, metal, rock and hiphop genres. Participants were asked to listen to audio music tracks of 1 min for each genre in a noise free environment. The main objectives of this study were to determine the effect of different genres of music on human emotions and indicating age group that is more responsive to music. Thirty men and women of three different age groups (15–25 years, 26–35 years and 36–50 years) underwent through the experiment that also included self reported emotional state after listening to each type of music. Features from three different domains i.e., time, frequency and wavelet were extracted from recorded EEG signals, which were further used by the classifier to recognize human emotions. It has been evident from results that MLP gives best accuracy to recognize human emotion in response to audio music tracks using hybrid features of brain signals. It is also observed that rock and rap genres generated happy and sad emotions respectively in subjects under study. The brain signals of age group (26–35 years) gave best emotion recognition accuracy in accordance to the self reported emotions.

Preschoolers’ attribution of affect to music: A comparison between vocal and instrumental performance

Research has shown inconsistent results concerning the ability of young children to identify musical emotion. This study explores the influence of the type of musical performance (vocal vs. instrumental) on children’s affect identification. Using an independent-group design, novel child-directed music was presented in three conditions: instrumental, vocal-only, and song (instrumental plus vocals) to 3- to 6-year-olds previously screened for language development ( N = 76). A forced-choice task was used in which children chose a face expressing the emotion matching each musical track. All performance conditions comprised “happy” (major mode/fast tempo) and “sad” (minor mode/slow tempo) tracks. Nonsense syllables rather than words were used in the vocals in order to avoid the influence of lyrics on children’s decisions. The results showed that even the younger children were able to correctly identify the intended emotion in music, although “happy” music was more readily recognised and recognition appeared facilitated in the instrumental condition. Performance condition interacted with gender.

Backmasking in the yeast genome: encoding overlapping information for protein-coding and RNA degradation.

Backmasking is a recording technique used to hide a sound or message in a music track in reverse, meaning that it is only audible when the record is played backwards. Analogously, the compact yeast genome encodes for diverse sources of information such as overlapping coding and non-coding transcripts, and protein-binding sites on the two complementary DNA strands. Examples are the consensus binding site sequences of the RNA-binding proteins Nrd1 and Nab3 that target non-coding transcripts for degradation. Here, by examining the overlap of stable (SUTs, stable unannotated transcripts) and unstable (CUTs, cryptic unstable transcripts) transcripts with protein-coding genes, we show that the predicted Nrd1 and Nab3-binding site sequences occur at differing frequencies. They are always depleted in the sense direction of protein-coding genes, thus avoiding degradation of the transcript. However in the antisense direction, predicted binding sites occur at high frequencies in genes with overlapping unstable ncRNAs (CUTs), so limiting the availability of non-functional transcripts. In contrast they are depleted in genes with overlapping stable ncRNAs (SUTs), presumably to avoid degrading the non-coding transcript. The protein-coding genes maintain similar amino-acid contents, but they display distinct codon usages so that Nrd1 and Nab3-binding sites can arise at differing frequencies in antisense depending on the overlapping transcript type. Our study demonstrates how yeast has evolved to encode multiple layers of information—protein-coding genes in one strand and the relative chance of degrading antisense RNA in the other strand—in the same regions of a compact genome.

The use of emotionally arousing music to enhance memory for subsequently presented images

Emotion-enhanced memory occurs when an arousal response to an emotion stimulus strengthens memory consolidation. We tested whether listening to emotionally arousing music enhanced memory in this way. In a within-subjects design, 37 participants (18 to 50 years, 22 female) listened to two of their own highly enjoyed music tracks, two self-rated neutral tracks from other participants’ selections, and a five-minute radio interview. After each listening episode, participants memorised a unique array of 24 images. Subjective and physiological emotional arousal was monitored throughout the experiment and free recall of all images within the five image arrays was tested at the end. As predicted, compared to the music and non-music controls, self-selected enjoyed music elicited greater subjective and physiological changes consistent with emotion, and more details from images presented after enjoyed music were recalled than after listening to the radio interview. A multiple regression analysis revealed that physiological changes consistent with an emotional arousal response to enjoyed music reliably predicted memory. Further research with larger samples is needed to replicate these exploratory findings.

Design of Music Learning Assistant Based on Audio Music and Music Score Recognition

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

Scalable Discovery of Audio Fingerprint Motifs in Broadcast Streams With Determinantal Point Process Based Motif Clustering

In this paper, we study the scalable discovery of audio repetitive patterns/motifs in long broadcast streams, where two segments are said to be repetitive if their audio fingerprints are close to each other. In this task, as we are confined to handle limited variability, we can adapt an audio hashing technique, originally proposed for searching a given music clip in music tracks, to successfully devise a linear complexity similarity matching method with a new step of repeated interval formation. This is the first contribution of this paper. As the similarity matching is super fast and thus coarse, there are false alarms in the large number of pair-wise matches generated, which constitute a major source of noise. We propose applying subset selection to the original set of pair-wise matches based on determinantal point processes (DPPs), as a filtering step, to reduce the noise. The selected subset of pair-wise matches is then subjected to motif clustering. We successfully apply DPP-based subset selection to improve motif clustering, which has a nice property that favors both quality and diversity. This is the second contribution of this paper. The proposed method is thoroughly evaluated on a 9-hour real-world audio stream and is compared with several reference methods. The bootstrap technique is used for the significance test. It is shown that the similarity matching is computationally very efficient (above 100 times faster than real time), and the filtering step with DPPs can significantly improve the precision of motif discovery, without sacrificing the recall performance.

Genre-Adaptive Semantic Computing and Audio-Based Modelling for Music Mood Annotation

This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling are proposed. A technique called the ACTwg employs genre-adaptive semantic computing of mood-related social tags, whereas ACTwg-SLPwg combines semantic computing and audio-based modelling, both in a genre-adaptive manner. The proposed techniques are experimentally evaluated at predicting listener ratings related to a set of 600 popular music tracks spanning multiple genres. The results show that ACTwg outperforms a semantic computing technique that does not exploit genre information, and ACTwg-SLPwg outperforms conventional techniques and other genre-adaptive alternatives. In particular, improvements in the prediction rates are obtained for the valence dimension which is typically the most challenging core affect dimension for audio-based annotation. The specificity of genre categories is not crucial for the performance of ACTwg-SLPwg. The study also presents analytical insights into inferring a concise tag-based genre representation for genre-adaptive music mood analysis.

ID 56 – Fibromyalgia and psychoacustic stimulation: An EEG study on pain modulation

Objective Fibromyalgic Sindrome is characterized by chronic musculoskeletal pain, mood and sleep disorders and weakness. Classical analgesic therapies are often uneffective, for this reason we aim to study the effects of a non-pharmacological treatment: psychoacustic stimulation through binaural beats. Our purpose is to investigate cortical activation in patients and self-reporting pain intensity. Methods Five participants affected by fibromyalgia participated in the study. EEG was recorded while subjects listened to four binaural beats music tracks calibrated on delta, theta, alpha and beta frequency bands. Before and after the stimulation, subjects reported pain intensity perception trough a Visual Analogue Scale (VAS) and a Numeric Rating Scale (NRS). Results Results showed a generalized relaxation effect mirrored by increasing delta frequency in fronto-central area, and increasing alpha frequency over occipital areas. We observed also a decreasing of beta band power. Delta is even clearer during acoustic stimulations calibrated on this frequency band. Electrophysiological evidences are supported by a decrease of VAS and NRS scores after psychoacustic stimulation. Conclusions The effect of relaxation on pain perception and EEG activity allowed to suppose the effectiveness of such methods in treating fibromyalgic pain. Key message Binaural beats can represent an alternative non-pharmacological treatment for fibromyalgia.

Acute stress recovery through listening to Melomics relaxing music: A randomized controlled trial

ABSTRACTDaily life entails having to cope with many stressful situations. Although stress-related reactions could sometimes provoke impairments in physiological processes due to the frequency of exposure or the stress burden of the event, physiological recovery after coping with stressors is highly implied in the aversive consequences of stress. To analyze the effects of listening to relaxing music (generated by the Melomics computer system) on the cardiovascular recovery and subjective feelings of anxiety after undergoing an acute-stress episode, a double-blind randomized controlled trial was conducted in healthy adults (N = 24; M = 23.05 years, SD = 2.97). Participants reported their levels of psychiatric symptomatology and anxiety and were then exposed to a stress induction protocol. Afterward, they underwent a period of recovery where they would be exposed to either a relaxing music track or silence, depending on a random assignation. Heart-derived functioning and self-reported anxiety were monitored throughout the study stages. All the participants showed stress-related reactions throughout the study stages, as it was shown for the study outcomes. Regarding the effect of listening to music, participants who listened to relaxing music during the recovery stage showed higher levels of sample entropy than controls, highlighting a large effect size on this difference (η2partial = .59). Relaxing music promotes more adaptive emotional regulation after coping with an acutely stressful event. This study aims to shed light on the actual effects of music interventions, and encourage the use of music-based interventions on health services.

The Effects of Music Therapy on Depression Among Students

Background: Depression is among the commonest mental disorders. Most students experience different levels of depression. Therefore, developing modern strategies, other than medication therapy, is of paramount importance to the prevention and management of depression among students. Objectives: The aim of this study was to investigate the effects of music therapy on depression among male dormitory students of Birjand University of Medical Sciences, Birjand, Iran. Methods: This pretest-posttest quasi-experimental study was done on 236 male dormitory students, who had been recruited in the study through systematic random sampling. Eligible students completed the Beck Depression Inventory before and two months after the study intervention. The intervention included music therapy by using pieces of light soft music. The students were asked to listen to music tracks twenty minutes per day for two consecutive months. During these two months, they were monitored weekly to ensure their adherence to the intervention. The SPSS software (v. 16.0) was employed to analyze the data through running the Wilcoxon and the independent-sample t tests as well as the one-way analysis of variance at a significance level of 0.05. Results: About 37.7% of the students suffered from different levels of depression. There was a significant difference between the pretest and the posttest values of depression (P = 0.001). In other words, the study intervention significantly reduced the mean score of depression from 11.00 ± 9.32 to 8.43 ± 9.6. The study findings also indicated that score of depression in the students did not differ significantly with marital status, place of residence, faculties and college semesters (P > 0.05). Conclusions: The findings of the study indicated that music therapy is effective in alleviating depression. Therefore, it can be used to prevent and alleviate depression among dormitory students.

Research on the Interior Sound Quality in Hybrid Electric Vehicle

Even the overall level of vehicle interior noise of hybrid electric vehicle (HEV) reduced to a certain degree, the vibration and noise generated by the engine, motor, generator and power split have made greater effect on the vehicle interior sound quality in HEV. In order to research the feature of vehicle interior sound quality in HEV, the HEV is used to be the research object, the binaural noise sample of the driver when playing different kinds of music in the vehicle with the speed of sixty kilometers per hour is collected. ArtemiS is used to conduct frequency division processing, so as to obtain the relative weight of each frequency band and the overall noise. The tone, roughness and sharpness of sound quality subjective evaluation parameters are quantified, the SPSS is used to establish the linear regression model of the sample, and the best masking music tracks are found out. Then, the sound samples that contains the best music tracks and the simple vehicle interior noise are re-collected, the regression model and ArtemiS are used to predict the subjective evaluation value. The research results show that when adding the music, the tone degree rises and the lowering degree decreases, thus the disturbing degree reduces, which significantly improves the sound quality in the HEV.