Music Files Research Articles

Multi-note intelligent fusion method of music based on artificial neural network

In order to overcome the problems of low fusion efficiency and poor fusion quality of the traditional music multi-note fusion method, an intelligent fusion method of music multi-note based on artificial neural network is proposed. Establish a multi-note model, and analyse the basic signal structure of notes under this model. Collect the initial music audio file, and preprocess the audio file through the steps of parsing, noise reduction, filtering and calibration. Use artificial neural network to segment unit music, extract fundamental note, peak note and melody note, and output in the form of a single note. Extract the features of different notes, and realise the intelligent fusion of music multi-notes according to the feature extraction results. The experimental results show that, compared with the traditional fusion method, the fusion speed of the designed intelligent fusion method is increased by at least 12.6s, and the signal-to-noise ratio is increased by 1.23.

Piano Teaching of Music Education Major in Colleges

Music exists in almost all cultures, and emotional expression occupies a very special place in the nature of music. This article mainly studies piano teaching in music education majors in colleges and universities. In this paper, a rectangular window with a length of 1s, a sliding length of 0.5s per frame, and 50% overlap of the window length is used to divide each music file in the database into frames. The feature extraction and statistics are performed on the music signal in each frame respectively, and the local feature data set corresponding to the dynamic V and A annotations in the database can be obtained. When the pitch interval is 1, 2, and 3 notes respectively, the correct recognition rate of the 9 contours is very low, followed by 31%, 1.25%, 4.01%. The results show that cultural differences have a significant impact on the music perception of people with normal hearing.

Development and testing of an FPT.AI-based voicebot

In recent years, voicebot has become a popular communication tool between humans and machines. In this paper, we will introduce our voicebot integrating text-to-speech (TTS) and speech-to-text (STT) modules provided by FPT.AI. This voicebot can be considered as a critical improvement of a typical chatbot because it can respond to human’s queries by both text and speech. FPT Open Speech, LibriSpeech datasets, and music files were used to test the accuracy and performance of the STT module. For the TTS module, it was tested by using text on news pages in both Vietnamese and English. To test the voicebot, Homestay Service topic questions and off-topic messages were input to the system. The TTS module achieved 100% accuracy in the Vietnamese text test and 72.66% accuracy in the English text test. In the STT module test, the accuracy for FPT open speech dataset (Vietnamese) is 90.51% and for LibriSpeech Dataset (English) is 0% while the accuracy in music files test is 0% for both. The voicebot achieved 100% accuracy in its test. Since the FPT.AI STT and TTS modules were developed to support only Vietnamese for dominating the Vietnam market, it is reasonable that the test with LibriSpeech Dataset resulted in 0% accuracy.

The Relationship of Music-Sound, Technology and Internet

Nowadays, music creation, collaboration, and publication are easier because of technology. Most young generations have sent music data, made, sold, bought music files on the internet. This changed music processes certainly resulted in different outcomes. Listening and creating music by new means, can change music itself. Technology has simplified tools, and the internet has simplified the distance. But new problems and questions have been found. How were the internet and technology influenced the quality of music, music creator, music appreciator and the form of music. The aims of this research to determine the relationship between music, technology, and the internet, through behavior of the young generation. This study was qualitative research that used observations and unstructured interviews. In subsequent observations, participant-observer was chosen as an advanced research method to better understand existing phenomena. The result of observations and interviews were interpreted, then presented descriptively. This research used theory by Don Ihde that technology has three characteristics (1) material (2) used (3)relationship of human and tools. The result of this research is internet influenced music quality and human appreciation. Technology changed the way humans create music.

Patient experience using digital therapy “Vigo” for stroke patient recovery: a qualitative descriptive study

Background The digital assistant “Vigo” is a computer-generated artificial intelligence-based application that serves as a digital assistant to a stroke patient and his family. With its conversational chatbot and gamification elements it counsels, educates, and trains the stroke patient and patient's family on stroke, rehabilitation, care, and other related issues. Aim This study describes insights about The digital assitant “Vigo” usability from a patients' perspective. Methods Twelve patients tested the application at their home environment. Three semi-structured interviews were conducted with each participant to obtain information on the usability of the application. Deductive thematic analyses were used to analyze trancripts. Results Participants expressed their opinions on music, pictures, video and audio files, chat options, layout, text, name of application and stand that is used for placement of devices on which “Vigo” is installed on. All participants generally evaluated application as transparent, understandable, and handy. The overall design of the application was rated as good. Participants were mostly unsatisfied with difficulty level and diversity of exercises. Conclusions Participants had a positive attitude towards using tablet tehchnologies in their home environment. Users of digital assistant “Vigo” acknowledged its ability to support, give educational information and increase participation in therapeutic activities. Implications for rehabilitation Tablet application can support, give educational information, and increase participation in therapeutic activities for persons after stroke. As home-based rehabilitation tool, the content of the application must be simple, flexible, and diverse, to face the challenges of meeting each individual's goals, functional needs and abilities.

Analytical Comparison of Classification Models for Raga Identification in Carnatic Classical Instrumental Polyphonic Audio

“MUSIC is the divine way of portraying the most beautiful about this world”. With that being said, the diversity in this language of music is immense, to say the least. Broadly, one would be well aware of the classification between Indian classical music and western music. In music Information Retrieval (MIR), raga classification has a tremendous role in understanding the fundamentals of Indian classical music and in a multitude of other tasks like database organisation of music files to music recommendation systems. The paper incorporates a variety of techniques like ANN, CNN, Bi LSTM and XGBoost models for the task of Raga Identification from a Carnatic Classical Instrumental audio (CCIA). The work is initially carried out on a set of 10 ragas and then extended to largely available 15 ragas of the dataset. The data samples for the same were obtained from the standard data set. This task showed state-of-the-art results with an accuracy of 97% for a set of 15 Ragas. The astounding results were obtained without performing source separation on the musical audio track. The process was carried out on the Ragas pertaining to Carnatic Classical music, a division of Indian classical music.

Tablet computer-supported conversation between people with dementia and their carers: technology as interactional focus

The purpose of the study is to explore when and how technology becomes a topic in interactions involving people with dementia and their carers. Three dyads of older women with dementia and their carers participated in the study. The dyads interacted in the home environments of the persons with dementia using tablet computers and two web-based applications with generic pictures, videos, and music files (CIRCA) and personalized pictures and films (CIRCUS). The data included twenty-one video-recorded interactions. Topical episode analysis and transcripts of interaction were used to analyze and exemplify when and how technology was talked about in the dyads. The dyads were engaged in exploring the tablets, and six common ways of making technology a topic of conversation were identified: talk about tech problems, commenting actions, expressing uncertainty in navigation, instructing and explaining, expressing surprise, and talk about technical development. The dyads explored the tablets in ways that were reflected in the content of their conversations. If people with dementia and their carers should benefit from today’s technology, such as there is evidence for the interactions examined in this study, their homes and daily environments must be equipped with sufficient internet access and technical support.

IMusic: a session-sensitive clustered classical music recommender system using contextual representation learning

Music has emerged to be of paramount importance to humanity and is not only considered as a source of entertainment but also an agent that causes social and psychological influences. A large number of existing digital music libraries have improved awareness among people through the music recommendation approach. However, several critical technical challenges still require attention and must be well–addressed to provide a reliable recommendation to music listeners. In Indian Classical Music, Raga is composed of coalescing diverse musical note structures. The history of classical music unveils that every raga possesses some distinct sessions. With a vast collection of classical music files in online music libraries, locating and listening to classical music is no more a difficult task. However, searching and listening to the audio of one’s preference may not be simple as it must instinctively satisfy the listener’s preference in a precise session. In this paper, a system termed as iMusic has been proposed to classify, analyze, and recommend the session–sensitive performance of Indian classical music by analyzing the musical note structures followed by features matching. Available note-patterns in the raga performance have been illustrated using a deep neural network and a set of machine learning algorithms where raga samples have been represented as inputs in the projected network and are classified based on the performing sessions. A context-aware k-means clustering algorithm has also been illustrated, entitled as a data filtering algorithm. The proficiency of the filtering algorithm has been established in two ways. Primarily, as a data-sensitive analysis, and second as empirical studies on synthetically obtained real classical music dataset ( https://github.com/SamarjitRoy89/iMusic.git ) to put on the hybrid music recommendation. In this work, a case-study of session-sensitive Indian music recommender system has been demonstrated using key-strategies viz. listener modelling, representation learning, and music profiling. Eventually, several evaluation metrics have been discussed to characterize the effectiveness of proposed representation learning-based playing session-sensitive music recommendation strategies. The proposed iMusic system renders data classification accuracy of ~ 88%. Such a framework could provide a useful basis regarding studies on hybrid music recommendation systems based on the usefulness of end-users.

Freedom of choice: Examining music listening as a function of favorite music format.

With so many formats available for individuals to use to listen to music, the present research adopted a Uses and Gratifications approach to investigate why people prefer particular formats. Specifically, the present study considered six formats: physical, digital file, freestreaming, paid-for streaming, radio, live music. A sample of 396 people (Mage = 34.53) completed an online survey, detailing the reasoning for their favourite format via a free-text response. Live music and digital files were the most popular formats. A thematic analysis of the uses and gratifications pertaining to each format highlighted how participants were attuned to the advantages (and disadvantages) of different formats, demonstrating an awareness of, and consideration relative to, rival formats. Findings suggest that choosing to listen to music across different formats may satisfy different needs, and that people demonstrate an awareness of their preference relative to the other available options.

Signal processing via sampled-data control theory

Digital sounds and images are used everywhere today, and they are all generated originally by analogue signals. On the other hand, in digital signal processing, the storage or transmission of digital data, such as music, videos or image files, necessitates converting such analogue signals into digital signals via sampling. When these data are sampled, the values from the discrete, sampled points are kept while the information between the sampled points is lost. Various techniques have been developed over the years to recover this lost data, but the results remain incomplete. Professor Yutaka Yamamoto's research is focused on improving how we can recover or reconstruct the original analogue data.

The music streaming metaphor and its underlying tangle of transcodes

ABSTRACT This article presents an interrogation of streamification, a concept that addresses the influence of streaming software on cultural artifacts and uses. I argue that the streamification builds on an underlying tangle of transcodes. I base this on empirical findings from a qualitative study of digital online music uses and the technologies that operate beyond the interface of streaming media. I map the typical online path of music files and exemplify the tangle of transcodes with three empirical cases and detail their meaning-making processes: 1) The most informal and tangled, when a listener is stream-ripping from YouTube. 2) The re-appropriation of informal YouTube material by a musician. 3) The more formal transcoding performed by a distributor. By drawing on metaphor theory and software studies, I discuss cultural transcoding and metaphor as central to streamification and discuss how a focus on ease-of-use inevitably hides the tangle of transcodes.

From print to digital: First steps in collecting digital music publications in UK legal deposit libraries

As a result of the 2013 Non-Print Legal Deposit Regulations, the United Kingdom’s legal deposit libraries acquired two large collections of digital music publications in PDF format: 43,165 from Music Sales and 13,167 from Faber Music. These constitute their back catalogues for the period 2013 to 2018. This article considers the genres of music that were collected, the relationship between printed and digital output and intended users. The increasing prevalence of digital publication in the United Kingdom’s music publishing industry was evident, with 99% of the content collected from these firms in this period in digital format. The ‘near duplication’ of content through the production of variant editions contributed greatly to the volume of output. Although 71% of content was popular music, other musical genres were also represented. Most of the publications were intended for performance rather than academic study. Despite the tendency to reprint extracts from printed publications digitally, there is currently little potential to switch UK music publishers from print to digital deposit. To truly reflect the nation’s cultural heritage, future collecting will need to embrace the breadth of the United Kingdom’s digital music publishing industry. Legal deposit libraries will need to collect publications with accompanying audiovisual content, those in multiple parts, proprietary music notation file formats and interactive content delivered via apps. This will require workflows that can accommodate publishers producing a handful of publications and those publishing at scale. The sustainability of collecting relies on close cooperation between libraries, composers, publishers, distributors, aggregators and music notation software providers.

Music Genre Classification using Lyric Mining Based ontf-Idf

With the advancement in the internet technologies, music domain has flourished with better access to various music libraries. In the present times, we are able to access music files over the internet with ease. Nowadays, the lyrics sets are categorised into different genres which cater to various listener moods. Users prefer to listen to music that best suits their mood. Thus considering the need for such classification, research works are being carried out to develop methodologies that can distinguish the music based on individual mood. In this research, instead of using the traditional method of audio feature analysis, we propose to develop a system which analyses the lyrics dataset of the songs based on the features extracted from the training phase and we can predict the mood of the song that is presented to the system at the validation stage. The proposed system is considering five moods containing one hundred songs each, for the validation purpose. The system is capable of predicting the mood of the song based on the analysis of the lyric text.

Retrospective Analysis of Plagiaristic Practices within a Cinematic Industry in India \u2013 a Tip in the Ocean of Icebergs

Music plagiarism is defined as using tune, or melody that would closely imitate with another author’s music without proper attributions. It may occur either by stealing a musical idea (a melody or motif) or sampling (a portion of one sound, or tune is copied into a different song). Unlike the traditional music, the Indian cinematic music is extremely popular amongst the public. Since the expectations of the public for songs that are enjoyable are high, many music directors are seeking elsewhere to “borrow” tunes. Whilst a vast majority of Indian cinemagoers may not have noticed these plagiarised tunes, some journalists and vigilant music lovers have noticed these activities. This study has taken the initiative to investigate the extent of plagiaristic activities within one Indian cinematic music industry. A list of plagiarised songs was produced by using YouTube® searches for “comparative videos” made by the vigilant music lovers about accused/detected music plagiarism. Some of these individuals were also interviewed to understand their views on this. During the investigation, it was possible to identify a vast number of plagiarised tunes, snippets, or even the full songs. In fact, some of these examples’ dates to 1954, during the era when no one would have noticed plagiarism. The paper would highlight the similarities of these music files. It will also show some examples of the excuses/denial given by the composers and would try to highlight the attitudes of general public towards these types of activities.

Integration of nonparametric fuzzy classification with an evolutionary-developmental framework to perform music sentiment-based analysis and composition

Over the past years, several approaches have been developed to create algorithmic music composers. Most existing solutions focus on composing music that appears theoretically correct or interesting to the listener. However, few methods have targeted sentiment-based music composition: generating music that expresses human emotions. The few existing methods are restricted in the spectrum of emotions they can express (usually to two dimensions: valence and arousal) as well as the level of sophistication of the music they compose (usually monophonic, following translation-based, predefined templates or heuristic textures). In this paper, we introduce a new algorithmic framework for autonomous music sentiment-based expression and composition, titled MUSEC, that perceives an extensible set of six primary human emotions (e.g., anger, fear, joy, love, sadness, and surprise) expressed by a MIDI musical file and then composes (creates) new polyphonic (pseudo) thematic, and diversified musical pieces that express these emotions. Unlike existing solutions, MUSEC is: (i) a hybrid crossover between supervised learning (SL, to learn sentiments from music) and evolutionary computation (for music composition, MC), where SL serves at the fitness function of MC to compose music that expresses target sentiments, (ii) extensible in the panel of emotions it can convey, producing pieces that reflect a target crisp sentiment (e.g., love) or a collection of fuzzy sentiments (e.g., 65% happy, 20% sad, and 15% angry), compared with crisp-only or two-dimensional (valence/arousal) sentiment models used in existing solutions, (iii) adopts the evolutionary-developmental model, using an extensive set of specially designed music-theoretic mutation operators (trille, staccato, repeat, compress, etc.), stochastically orchestrated to add atomic (individual chord-level) and thematic (chord pattern-level) variability to the composed polyphonic pieces, compared with traditional evolutionary solutions producing monophonic and non-thematic music. We conducted a large battery of tests to evaluate MUSEC’s effectiveness and efficiency in both sentiment analysis and composition. It was trained on a specially constructed set of 120 MIDI pieces, including 70 sentiment-annotated pieces: the first significant dataset of sentiment-labeled MIDI music made available online as a benchmark for future research in this area. Results are encouraging and highlight the potential of our approach in different application domains, ranging over music information retrieval, music composition, assistive music therapy, and emotional intelligence.

Music Cryptography based on Carnatic Music

Music and cryptography have been linked to one another since ancient times. The idea of replacing plaintext letters with music notes and sending the music file to receiver, is not new. But such replacements sometimes result in music clips which are not pleasant to listeners and thereby leading to the music clip gaining unnecessary extra attention. Most of the works done in this area, fail to ensure the generation of a music clip that invariably conforms to any particular form of music. Melody of the music clip is neglected. In order to address this issue, current paper proposes a novel approach for sharing a secret message based on concepts of Carnatic Classical Music. The method proposed here aims at converting a message in textual format to a music clip before sending it to the receiver. Receiver can then decrypt that message using the knowledge of range of frequency values associated with each musical note also called as 'swara' in Carnatic Classical Music. Each plaintext character from English alphabet is replaced by different combinations of swaras. The set of swaras mapped to each plaintext character is so chosen that the final music file produced as the output of encryption always conforms to a melodic form ('Raga') governed by the framework of Carnatic Classical Music. Ten subject matter experts in the field of Carnatic music have given their opinion about the conformance of these music clips to specified ragas. Also, Mean Opinion Score (MOS) of 25 listeners has been tabulated to test and verify the melodic aspect of these music clips.

Klasifikasi Genre Musik Menggunakan Metode Mel-Frequency Cepstrum Coefficients dan K-Nearest Neighbors Classifier

In the world of music, music has several types of genre genres that can be grouped, there are music genre pop, rock, blues, slow, jazz, metal, dangdut and many more. And for each person must have a genre favorite that is different from one another, but to distinguish it does not need to play music files one by one especially if the number of music files is a lot. Therefore, computer software is needed to distinguish each of these genres in order to make it easier for users to distinguish and group the types of music according to their wishes automatically. By using the MFCC and KNN methods as a classification solution to classify several types of genre streams can be easily resolved. The results achieved from this study reached 52,4% with a K = 13 as the nearest neighboring point.

Music Recommendation System Using Human Activity Recognition From Accelerometer Data

Music listening is a very personal and situational behavior for modern people who always carry smartphones in everyday life. Therefore, contextual information, such as user’s current activity and mood state could be used to greatly improve music recommendations. In this paper, we develop a smartphone-based mobile system that includes two core modules for recognizing human activities and then accordingly recommending music. In the proposed method, a deep residual bidirectional gated recurrent neural network is applied to obtain high activity recognition accuracy from accelerometer signals on the smartphone. In order to improve the performance of tempo-oriented music classification, an ensemble of dynamic classification using a long-term modulation spectrum and sequence classification using a short-term spectrogram is used. Music recommendation is performed using the relationship between the recognized human activities and the music files indexed by tempo-oriented music classification that reflects user preference models in order to achieve high user satisfaction. The results of comprehensive experiments on real data confirm the accuracy of the proposed activity-aware music recommendation framework.

Real-Time Optical Music Recognition System for Dulcimer Musical Robot

Traditional optical music recognition (OMR) is an important technology that automatically recognizes scanned paper music sheets. In this study, traditional OMR is combined with robotics, and a real-time OMR system for a dulcimer musical robot is proposed. This system gives the musical robot a stronger ability to perceive and understand music. The proposed OMR system can read music scores, and the recognized information is converted into a standard electronic music file for the dulcimer musical robot, thus achieving real-time performance. During the recognition steps, we treat note groups and isolated notes separately. Specially structured note groups are identified by primitive decomposition and structural analysis. The note groups are decomposed into three fundamental elements: note stem, note head, and note beams. Isolated music symbols are recognized based on shape model descriptors. We conduct tests on real pictures taken live by a camera. The tests show that the proposed method has a higher recognition rate.

Polyphonic pitch perception in rooms using deep learning networks with data rendered in auditory virtual environments

This paper proposes methods for generation and implementation of uniform, large-scale data from auralized MIDI music files for use with deep learning networks for polyphonic pitch perception and impulse response recognition. This includes synthesis and sound source separation of large batches of multitrack MIDI files in non-real time, convolution with artificial binaural room impulse responses, and techniques for neural network training. Using ChucK, individual tracks for each MIDI file, containing the ground truth for pitch and other parameters, are processed concurrently with variable Synthesis ToolKit (STK) instruments, and the audio output is written to separate wave files in order to create multiple incoherent sound sources. Then, each track is convolved with a measured or synthetic impulse response that corresponds to the virtual position of the instrument in the room before all tracks are digitally summed. The database now contains the symbolic description in the form of MIDI commands and the auralized music performances. A polyphonic pitch model based on an array of autocorrelation functions for individual frequency bands is used to train a neural network and analyze the data [Work supported by IBM AIRC grant and NSF BCS-1539276.]