Field Of Music Information Retrieval Research Articles

Music Genre Classification

The Music Genre Classification model automatically divides music into different genres using a small number of audio files and a range of musical attributes. This topic is highly relevant to the field of music information retrieval since it provides a way to organize and analyze large amounts of music files. For MGC, standard machine learning techniques such as SVM, KNN, Decision trees, and neural networks can be applied. These algorithms are trained to recognize different musical qualities and traits, which allows them to categorize the audio files into different genres. Numerous applications show that deep learning algorithms—such as CNN, ANN, and others—perform better than conventional machine learning algorithms. Consequently, the CNN method is adjusted to perform the categorization of music files. This classifies musical genres using deep learning methods from CNN. To evaluate the effectiveness of the MGC algorithms, accuracy is used. Moreover, the impact of different algorithms on MGC performance can be compared and studied. It can be applied to automated music recommendation systems, music production, and music education.

A regularization algorithm for local key detection

In the field of music information retrieval, the detection of global key in both popular and classical music has been studied extensively, but local key detection has been studied to a lesser extent, even though modulation is an important component of compositional style. It is particularly challenging to identify key change boundaries correctly. We modeled this task as an optimization problem, that of finding out how to divide a piece into sections in different keys taking into consideration both the quality of the fit between the key and the section and the number of sections. We determined the optimal assignment of key to section using the Krumhansl–Schmuckler algorithm with a slightly modified version of the Krumhansl and Kessler key profile. We included a regularization algorithm in the formulation of our problem to balance the number of sections and avoid superfluous modulations. Using a dataset of 80 randomly chosen pieces of music in a variety of genres and levels of complexity, we compared our algorithm with a hidden Markov model (HMM) to determine which method is better for identifying local key. Our approach yielded significantly more accurate results and suggests future avenues of research.

MCSSME: Multi-Task Contrastive Learning for Semi-supervised Singing Melody Extraction from Polyphonic Music

Singing melody extraction is an important task in the field of music information retrieval (MIR). The development of data-driven models for this task have achieved great successes. However, the existing models have two major limitations: firstly, most of the existing singing melody extraction models have formulated this task as a pixel-level prediction task. The lack of labeling data has limited the model for further improvements. Secondly, the generalization of the existing models are prone to be disturbed by the music genres. To address the issues mentioned above, in this paper, we propose a multi-Task contrastive learning framework for semi-supervised singing melody extraction, termed as MCSSME. Specifically, to deal with data scarcity limitation, we propose a self-consistency regularization (SCR) method to train the model on the unlabeled data. Transformations are applied to the raw signal of polyphonic music, which makes the network to improve its representation capability via recognizing the transformations. We further propose a novel multi-task learning (MTL) approach to jointly learn singing melody extraction and classification of transformed data. To deal with generalization limitation, we also propose a contrastive embedding learning, which strengthens the intra-class compactness and inter-class separability. To improve the generalization on different music genres, we also propose a domain classification method to learn task-dependent features by mapping data from different music genres to shared subspace. MCSSME evaluates on a set of well-known public melody extraction datasets with promising performances. The experimental results demonstrate the effectiveness of the MCSSME framework for singing melody extraction from polyphonic music using very limited labeled data scenarios.

Evolution and Emerging Trends in Musical Information Retrieval: A Comprehensive Review and Future Prospects

The rapid development of the digital music industry has brought challenges for music lovers and researchers. In response to these challenges, the field of Music Information Retrieval (MIR) emerged in the mid-1960s to capture the complicated and multi-layered nature of music. Among the various approaches explored, machine learning methods have shown promise in overcoming the complexity of this interdisciplinary field. This paper is primarily centered on conducting an in-depth review of the current state of research regarding the utilization of machine learning within the field of Music Information Retrieval (MIR). Additionally, it aims to forecast the potential future directions within the MIR industry. Upon extensive literature review, it becomes evident that various machine learning techniques, including Neural Networks (NN), Support Vector Machines (SVM), and K-nearest neighbors (KNN), have found common applicability in this field. Furthermore, this review highlights Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) as potential algorithms poised to shape the future landscape of MIR. The findings of this paper serve to elucidate the direction in which MIR is progressing, offering valuable guidance for forthcoming research and development endeavors. In doing so, it contributes to the continued progress and maturation of the field MIR.

Music emotion classification using a hybrid CNN-LSTM model

The emotional content of music, interwoven with the intricacies of human affect, poses a unique challenge for computational recognition and classification. With the digitalization of music libraries expanding exponentially, there is a pressing need for precise, automated tools capable of navigating and categorizing vast musical repositories based on emotional contexts. This study advances music emotion classification in the field of music information retrieval by developing a deep learning model that accurately predicts emotional categories in music. The goal of this research is to advance the field of music emotion classification by leveraging the capabilities of convolutional neural networks combined with long short-term memory within deep learning frameworks. The contribution of this study is to provide a refined approach to music emotion classification, combining the power of convolutional neural networks and long short-term memory architectures with sophisticated preprocessing of the Emotify dataset for a deeper and more accurate analysis of musical emotions. The research introduces a novel architecture combining Convolutional Neural Networks and Long Short-Term Memory networks designed to capture the intricate emotional nuances in music. The model leverages convolutional neural networks for robust feature detection and Long Short-Term Memory networks for effective sequence learning, addressing the temporal dynamics of musical features. Utilizing the Emotify dataset, comprising tracks annotated with nine emotional features, the study expands the dataset by segmenting each track into 20 parts, thereby enriching the variety of emotional expressions. Techniques like the synthetic minority oversampling technique were implemented to counter dataset imbalance, ensuring equitable representation of various emotions. The spectral characteristics of the samples were analyzed using the Fast Fourier Transform, contributing to a more comprehensive understanding of the data. Through meticulous fine-tuning, including dropout implementation to prevent overfitting and learning rate adjustments, the developed model achieved a notable accuracy of 94.7 %. This high level of precision underscores the model's potential for application in digital music services, recommendation systems, and music therapy. Future enhancements to this music emotion classification system include expanding the dataset and refining the model architecture for even more nuanced emotional analysis

Augmentation Embedded Deep Convolutional Neural Network for Predominant Instrument Recognition

Instrument recognition is a critical task in the field of music information retrieval and deep neural networks have become the dominant models for this task due to their effectiveness. Recently, incorporating data augmentation methods into deep neural networks has been a popular approach to improve instrument recognition performance. However, existing data augmentation processes are always based on simple instrument spectrogram representation and are typically independent of the predominant instrument recognition process. This may result in a lack of coverage for certain required instrument types, leading to inconsistencies between the augmented data and the specific requirements of the recognition model. To build more expressive instrument representation and address this inconsistency, this paper constructs a combined two-channel representation for further capturing the unique rhythm patterns of different types of instruments and proposes a new predominant instrument recognition strategy called Augmentation Embedded Deep Convolutional neural Network (AEDCN). AEDCN adds two fully connected layers into the backbone neural network and integrates data augmentation directly into the recognition process by introducing a proposed Adversarial Embedded Conditional Variational AutoEncoder (ACEVAE) between the added fully connected layers of the backbone network. This embedded module aims to generate augmented data based on designated labels, thereby ensuring its compatibility with the predominant instrument recognition model. The effectiveness of the combined representation and AEDCN is validated through comparative experiments with other commonly used deep neural networks and data augmentation-based predominant instrument recognition methods using a polyphonic music recognition dataset. The results demonstrate the superior performance of AEDCN in predominant instrument recognition tasks.

Analyzing the Performance of Machine Learning Models in Music Genre Classification

Abstract: Music genre classification is a fundamental task in the field of music information retrieval (MIR) and has gained significant attention in recent years due to the rapid growth of digital music collections. This research paper presents a comprehensive review of the application of machine learning techniques for music genre classification. We explore various methodologies, feature extraction techniques, and classification algorithms used in the domain, highlighting their strengths, limitations, and recent advancements. The objective of this paper is to provide researchers and practitioners with a comprehensive understanding of the current state-of- the-art approaches, challenges, and future directions in music genre classification using machine learning.

Automatic Label Calibration for Singing Annotation Using Fully Convolutional Neural Network

Accurately‐labeled data is crucial for the training of machine learning models. For singing‐related tasks in the music information retrieval field, accurately‐labeled data is limited because annotating singing is time‐consuming. Several studies create vocal datasets using a two‐step annotation method which creates coarse labels first and then executes a manual calibration procedure. However, manually calibrating coarsely‐labeled singing data is expensive and time‐consuming. To address this problem, in this study we propose a singing‐label calibration framework, which aims to automatically calibrate the coarsely‐labeled singing data with higher accuracy. This framework contains a data augmentation method to generate training and testing data, a reasonable data preprocessing method to handle music audio and symbolic labels, a fully‐convolutional neural network to estimate the difference between coarse labels and accurate labels, and a novel calibration function to correct the coarse labels. Various experiments are conducted to examine the effect of our research. The results show that our model can highly reduce the cost time and slightly increase the labeling accuracy of the manual calibration process. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Music Similarity Estimation based on Feature Extraction and Deep Learning

The field of music information retrieval (MIR) has developed rapidly in recent years, which provides a lot of convenience for people's entertainment life after combining with big data and deep learning. This paper studies music similarity estimation in the field of music information retrieval. To be specific, based on the deep learning of music feature data, the music similarity model (a classifier) is obtained to judge whether the music similarity reaches more than 50% (i.e., random model). According to the experiment, using the Siamese neural network to train the model with chroma features as input can achieve high accuracy. It will make it more convenient for music recommendation, humming recognition and plagiarism identification. In the future, more researches ought to focus on the real value instead of classifier to judge the similarity or at least the clustering model (multiple thresholds instead of two). Anyway, these results shed light on guiding further exploration focusing on similarity extraction of music in the context of the state-of-art deep learning scenarios.

Instrument Classification Using Different Machine Learning and Deep Learning Methods

Instruments are categorized into the 5 groups in the Sachs-Hornbostel system: idiophones, membranophones, aerophones, chordophones, and electrophones. It might be easy to tell the Sachs-Hornbostel group that an instrument belongs to. However, distinguishing single instrument sound can be hard in monophonic or polyphonic music pieces and it is an important subject for musicians. Using computer science models can help musicians to analyze songs easily and fasten the speed of finding the instrument that are wanted by music producers or composers. This work aims to compare different models on particular instruments (monophonic sound) recognition which is an important problem in the field of music information retrieval. Jupyter Notebook is included for easy reproducibility. Among the six models chosen in this research: k-nearest neighbors(kNN), Support Vector Machines(SVM), Gaussian Mixture Modeling(GMM), Artificial Neural Networks(ANN), Convolutional Neural Networks(CNN) and Recurrent Neural Networks(RNN), CNN is the most accurate model and SVM is the fastest model while CNN has the prospect of being improved because it can be adjusted manually.

Automated Music Genre Classification through Deep Learning Techniques

Music Genre Classification (MGC) automatically categorizes music into different genres based on various musical attributes and features in a small number of music files. This is a crucial problem in the field of music information retrieval as it provides a way to organize and analyse large amounts of music files. MGC can be performed using conventional machine learning algorithms such as SVM, k-nearest neighbours, Decision trees, and neural networks. These algorithms learn to recognize different musical features and attributes to categorize the music files into different genres. The literature shows that the performance of conventional machine learning algorithms is inferior to deep learning algorithms such as CNN, RNN, etc., in various applications. Hence, the CNN algorithm is adapted to implement the classification of music files. This aims to classify music genres using CNN deep learning techniques. The performance of the algorithms for MGC can be evaluated using metrics such as accuracy, precision, recall, and F1-score. Additionally, the impact of different features and algorithms on the performance of MGC can be studied and compared. It has applications in areas such as automated music recommendation systems, music education, and music production. An accuracy of 83% is achieved by using CNN to accomplish the task of MGC.

VAT-SNet: A Convolutional Music-Separation Network Based on Vocal and Accompaniment Time-Domain Features

The study of separating the vocal from the accompaniment in single-channel music is foundational and critical in the field of music information retrieval (MIR). Mainstream music-separation methods are usually based on the frequency-domain characteristics of music signals, and the phase information of the music is lost during time–frequency decomposition. In recent years, deep learning models based on speech time-domain signals, such as Conv-TasNet, have shown great potential. However, for the vocal and accompaniment separation problem, there is no suitable time-domain music-separation model. Since the vocal and the accompaniment in music have a higher synergy and similarity than the voices of two speakers in speech, separating the vocal and accompaniment using a speech-separation model is not ideal. Based on this, we propose VAT-SNet; this optimizes the network structure of Conv-TasNet, which sets sample-level convolution in the encoder and decoder to preserve deep acoustic features, and takes vocal embedding and accompaniment embedding generated by the auxiliary network as references to improve the purity of the separation of the vocal and accompaniment. The results from public music datasets show that the quality of the vocal and accompaniment separated by VAT-SNet is improved in GSNR, GSIR, and GSAR compared with Conv-TasNet and mainstream separation methods, such as U-Net, SH-4stack, etc.

A Postcolonial MIR?

The field of music information retrieval (MIR) offers tools for the analysis of music and sound, for example in archives of historical recordings; but it has also been widely critiqued for bias toward Western music and Western epistemologies. There seems to be a gap, or even an irresolvable friction, between computer science-based MIR tools, problems, and methodologies, on the one hand, and, on the other hand, humanities-based research in music and media that examines sound from critical and postcolonial perspectives. This essay draws from sound studies and critical technology studies to propose that this is an opportune moment for interdisciplinary collaborations between humanities scholars and technologists to enhance critical analyses of music and sound. Key to such collaborations would be a consideration of ontological differences between the two approaches, such as divergent understandings of the fundamental concepts of music, genre, and difference. The essay takes several examples from the author’s research into sound and Dutch colonialism at the Netherlands Institute for Sound and Vision media archive in order to propose some examples of how MIR’s technological tools could potentially enhance critical humanities-driven analyses of sound, perception, and colonial difference. More broadly, the essay argues, the divergences and even tensions between disciplines can be productive for scholarly reflexivity, postcolonial or anticolonial scholarship, and applications of technological tools toward social justice goals.

Recognition of Continuous Music Segments Based on the Phase Space Reconstruction Method.

Piano score recognition is one of the important research contents in the field of music information retrieval, and it plays an important role in information processing. In order to reduce the influence of vocals on the progress of piano notes and restore the harmonic information corresponding to piano notes, the article models the harmonic information and vocal information corresponding to piano notes in the frequency spectrum. We use the phase space reconstruction method to extract the nonlinear feature parameters in the note audio and use some of the parameters as the training set to construct the support vector machine (SVM) classifier and the other part as the test set to test the recognition effect. Therefore, the method of adaptive signal decomposition and SVM is introduced into the signal preprocessing link, and the corresponding recognition process is established. In order to improve the performance of the support vector machine, the article uses measurement learning method to obtain the measurement learning and uses the measurement learning to replace the Euclidean distance of the Gaussian kernel function of the support vector machine. The SVM method of adaptive signal decomposition and the SVM method of principal component analysis are introduced into the preprocessing process of the note signal, and then the preprocessed signal is reconstructed in phase space, and the corresponding recognition process is established. The method of directly reconstructing the phase space of the original signal has higher accuracy and can be applied to the note recognition of continuous music segments. The final experimental results show that, compared with the current popular piano score recognition algorithm, the recognition accuracy of the proposed piano score recognition algorithm is improved by 3.5% to 12.2%.

Automatic Music Classification Model Based on Instantaneous Frequency and CNNs in High Noise Environment.

Automatic music classification has significant research implications because it is the foundation for quick and efficient music resource retrieval and has a wide range of possible applications. In this study, DL is used to extract and categorize musical features, and a DL-based model for music feature extraction and classification is created. In this study, the instantaneous frequency and short-time Fourier transform are used to estimate the sine of a mixed music signal. Based on peak-frequency pairs, the DL algorithm is then used to calculate multiple candidate pitch estimates for each frame, and the melody pitch sequence is then obtained in accordance with the pitch profile duration and continuity characteristics. With this approach, the pitch can be calculated without reference to the fundamental frequency component. A music feature classification approach using spectrogram as input data and CNN as classifier is proposed at the same time in light of CNN's benefits in image processing. Studies reveal that this model's categorization and music feature extraction accuracy is as high as 94.18 percent and 95.66 percent, respectively. The outcomes demonstrate the efficiency of this technique for the extraction and classification of musical features. The field of music information retrieval is a good fit for it.

Construction and Application of a Piano Playing Pitch Recognition Model Based on Neural Network.

The intonation recognition of piano scores is an important problem in the field of music information retrieval. Based on the neural network theory, this study constructs a piano playing intonation recognition model and uses the optimized result as the feature of piano music to realize the prediction of the music recognition of the intonation preference. The model combines the behavioral preference relationship between intonation and musical notation to measure the similarity between intonations, which is used to calculate the similarity between intonation preference and music, and solves the quantification problem of intonation recognition. In the simulation process, the pitch preference feature of piano playing is used as the identification basis, and the effectiveness of the algorithm is verified through four sets of experiments. The experimental results show that the average symbol error rate of the improved network model is reduced to 0.3234%, and the model training time is about 33.3% of the traditional convolutional recurrent neural network, which is optimized in terms of recognition accuracy and training time in single-class pitch feature. In the recommended method of multi-category evaluation of pitch features, the recognition accuracy of multi-category pitch features is 42.89%, which effectively improves the musical tone recognition rate.

Bowing to Five Pecks of Rice? The Impact of Profit Expectancy on Artistic Creativity

Do independent and amateur producers in cultural industries curtail their creativity when they have higher expectations of economic returns? Previous studies on the “creative/commerce” pair of the cultural industries did not provide a clear answer. This paper tries to answer this question by studying the case of Chinesemusic.com, a music streaming platform that launched a monetization program, allowing musicians to directly profit from their songs on the platform. I collect 16,320 songs from the platform released before and after the launch of the monetization program and, using computational tools developed in the field of Music Information Retrieval, I propose a novel approach to measure the creativity of the songs based on their sound. The results suggest that whilst more independent and amateur musicians were attracted by the monetization program, their songs became less and less creative afterward with a more significant shift among the former. The findings indicate that independent and amateur producers will proactively make compromises in their creative activities when they are given resources for a more promising career in the business.

Online Music Style Recognition via Mobile Computing

Music is a widely used data format in the explosion of Internet information. Automatically identifying the style of online music in the Internet is an important and hot topic in the field of music information retrieval and music production. Recently, automatic music style recognition has been used in many real life scenes. Due to the emerging of machine learning, it provides a good foundation for automatic music style recognition. This paper adopts machine learning technology to establish an automatic music style recognition system. First, the online music is process by waveform analysis to remove the noises. Second, the denoised music signals are represented as sample entropy features by using empirical model decomposition. Lastly, the extracted features are used to learn a relative margin support vector machine model to predict future music style. The experimental results demonstrate the effectiveness of the proposed framework.

Insomnia Audio Therapy Mobile Application with Music Recommender System

With the quick advancement of Internet and artificial intelligence technologies, development of a robust and accurate music recommendation system has become an important issue in the field of music information retrieval. Music recommendation system has been widely used in many real-life applications, including in the health domain as an alternative of therapies. This paper presents the research design and implementation of music recommendation system that possible to be used as an insomnia audio therapy in a mobile application platform. The research focused on investigating the performances of three machine learning algorithms namely Random Forest, Decision Tree and Support Vector Machine to be selected as the music recommendation tool. For the machine learning training and testing purposes, data was collected based on the simulated run of the proposed insomnia audio therapy mobile application. The results indicated that Random Forest performed as the best machine learning algorithm in predicting the relevant music. The proposed mobile application with machine learning music recommender system will provide a basis for the realization of intelligent music therapy in treating insomnia disorder patients as well as in other music applications.

Digital Music Recommendation Technology for Music Teaching Based on Deep Learning

With the rapid development of music streaming media service industry, users can easily hear any song on mobile devices. Internet has become a huge music storage platform. With the development of network and large-scale digital music industry, the acquisition and listening of music are presented to users in a more convenient way. How to find the music loved by users from the massive Internet digital music data has become the key problem and main goal to be solved in the field of music information retrieval. Personalized music recommendation system can accurately find and push songs that users may be interested in from tens of millions of huge music libraries according to users’ information under the condition that users only have vague demand for listening to songs. Relying on the traditional search method to find the music that you are interested in can no longer meet the needs of users, so the current music recommendation system needs to dig out the music that has no clear needs in the long tail to help people find their favorite songs.