Music Recommendation System Research Articles

A Robust Emotion Detection and Music Recommendation System using Mini Xception CNN

Abstract: Due to the emerging developments in artificial intelligence(AI) and machine learning(ML) Technology various systems are developed in recent days that late the human emotions and real time aspects of human psychology detection. Facial recognition based music recommendation system (MRS) is a interesting area of research where he plays an important role in handling the psychology patients. Face recognition system is extensively applied in security systems surveillance systems fault identification etc. Based on Emotion of the human, the music recommendation needs to be provided to analyse the phycology changes with the patients. The proposed approach is focused on considering the constraints available with the facial recognition system in existing frameworks such as deep feature extraction processing delay need to be reduce the hair using deep convolutional neural network(DCNN) architecture based Mini exception algorithm is developed. The system considered FER2013 image dataset that contains 35000 face images with automated labels that would be helpful for the presented approach to identify the emotion class accurately. The Mini exception algorithm used in CNN layers act as a lightweight system compared with various states of approaches. The proposed system removes the barrier between the existing frameworks and achieved the accuracy of 92%. The recommended music is derived from the music database and further mapped with respect to the algorithm result.

Emotion based Music Recommendation System

Emotion based Music Recommendation System

Developing Music Recommendation System by Integrating an MGC with Deep Learning Techniques

In the modern scenario, everyone uses the internet to find music, movies, products, services and other commodities on a regular basis to make their lives easier. Because of a lot of data on millions of music, movie, products and services on websites, we need a recommender system very much to assist people in making decisions more quickly and easily. In this research study, we have developed an intelligent music recommendation system by integrating a Music Genre Classification (MGC) with different types of Deep Learning Techniques such as RNN-LSTM, GRU and CNN. We have used the GTZAN Genre dataset to training our system. We have extracted the features from GTZAN dataset by the help of Mel Frequency Cepstral Coefficients (MFCCs) then pass the MFCCs into our deep learning networks. After classifying the appropriate music genre, recommended the music from particular genre from the labelled database which has been classified by our system. From our proposed models the GRU, CNN and RNN-LSTM produced about 71%, 72% and 74% respectively in our testing accuracy. The RNN-LSTM has achieved the best accuracy result (74%) among all of our proposed models.

Retracted: Design and Construction of Hybrid Music Recommendation System Integrating Music Gene

Retracted: Design and Construction of Hybrid Music Recommendation System Integrating Music Gene

Machine Learning Based Music Genre Classification and Recommendation System

Music has an important role in our life. It is also known that music helps to relax and strengthen the human spirit. The widespread use of the Internet has led to significant changes and developments in the music industry. The increase and widespread use of online music listening and sales platforms, the constant updating of these platforms and the classification of music genres can be given as examples of these developments. Music genre classification is an important step for the music recommendation system. In order for music to be classified by individuals require to listen to many songs and choose their genre. This is a difficult process and waste of time. In this paper, it is aimed to classify music according to its genres by using machine learning algorithms and to suggest similar types of music to the user. For this purpose, the features of the music files were extracted with digital signal processing techniques, and the music genres were automatically detected by using machine learning algorithms with the obtained features and a recommendation system was developed. The GTZAN dataset was chosen to be used in the study. Eight different machine learning models were trained in the Jupyter Notebook environment and the findings were compared. For this purpose, the data set was split into two sets as 80% training and 20% testing, and the accuracy of the models was evaluated. Among the tested models, the most successful result was obtained with the XGBoost algorithm with an accuracy rate of 91,792%.

가사 키워드 추출을 활용한 지식 그래프 기반 음악 추천 시스템

콘텐츠 산업에서는 정보 과부하 문제에 대한 해결책으로 추천 시스템이 활용되고 있다. 그러나 기존의 추천 시스템은 데이터 희소성과 콜드 스타트에 대한 문제점을 가지고 있다. 이를 완화하기 위해 다양한 보조 데이터를 활용하는 방법들이 연구되고 있다. 본 연구의 목적은 노래의 가사 정보를 음악 추천 시스템에서 보조 데이터로 활용하는 방법을 제시함으로써 음악 추천 시스템의 성능을 향상시키는 데에 있다. 음악 추천 시스템의 성능 향상을 위한 방법으로, 가사 정보에서 키워드를 추출하여 보조 데이터로 활용한 지식 그래프 기반의 음악 추천 시스템을 제안한다. 본 연구의 음악 추천 시스템은 가사 키워드 추출에 자연어 임베딩 모델인 Sentence-BERT를 활용하였고, 음악 추천에 사용자 정보와 항목의 특징을 복합적으로 고려할 수 있는 KPRN을 활용하였다. 연구 결과, 본 연구의 가사 키워드 추출 방법을 활용한 추천 모델은 기존의 모델보다 추천 정확도가 높게 나타났다. 따라서 이러한 결과를 통해 가사 키워드 정보가 보조 데이터로써 지식 그래프 기반 음악 추천 시스템의 성능을 향상시킨다는 결론을 도출하였다.

Induced Emotion-Based Music Recommendation through Reinforcement Learning

Music is widely used for mood and emotion regulation in our daily life. As a result, many research works on music information retrieval and affective human-computer interaction have been proposed to model the relationships between emotion and music. However, most of these works focus on applications in a context-sensitive recommendation that considers the listener’s emotional state, but few results have been obtained in studying systems for inducing future emotional states. This paper proposes Moodify, a novel music recommendation system based on reinforcement learning (RL) capable of inducing emotions in the user to support the interaction process in several usage scenarios (e.g., games, movies, smart spaces). Given a target emotional state, and starting from the assumption that an emotional state is entirely determined by a sequence of recently played music tracks, the proposed RL method is designed to learn how to select the list of music pieces that better “match” the target emotional state. Differently from previous works in the literature, the system is conceived to induce an emotional state starting from a current emotion instead of capturing the current emotion and suggesting certain songs that are thought to be suitable for that mood. We have deployed Moodify as a prototype web application, named MoodifyWeb. Finally, we enrolled 40 people to experiment MoodifyWeb, employing one million music playlists from the Spotify platform. This preliminary evaluation study aimed to analyze MoodifyWeb’s effectiveness and overall user satisfaction. The results showed a highly rated user satisfaction, system responsiveness, and appropriateness of the recommendation (up to 4.30, 4.45, and 4.75 on a 5-point Likert, respectively) and that such recommendations were better than they thought before using MoodifyWeb (6.45 on a 7-point Likert).

Valence-arousal classification of emotion evoked by Chinese ancient-style music using 1D-CNN-BiLSTM model on EEG signals for college students.

During the COVID-19 pandemic, young people are using multimedia content more frequently to communicate with each other on Internet platforms. Among them, music, as psychological support for a lonely life in this special period, is a powerful tool for emotional self-regulation and getting rid of loneliness. More and more attention has been paid to the music recommender system based on emotion. In recent years, Chinese music has tended to be considered an independent genre. Chinese ancient-style music is one of the new folk music styles in Chinese music and is becoming more and more popular among young people. The complexity of Chinese-style music brings significant challenges to the quantitative calculation of music. To effectively solve the problem of emotion classification in music information search, emotion is often characterized by valence and arousal. This paper focuses on the valence and arousal classification of Chinese ancient-style music-evoked emotion. It proposes a hybrid one-dimensional convolutional neural network and bidirectional and unidirectional long short-term memory model (1D-CNN-BiLSTM). And a self-acquisition EEG dataset for Chinese college students was designed to classify music-induced emotion by valence-arousal based on EEG. In addition to that, the proposed 1D-CNN-BILSTM model verified the performance of public datasets DEAP and DREAMER, as well as the self-acquisition dataset DESC. The experimental results show that, compared with traditional LSTM and 1D-CNN-LSTM models, the proposed method has the highest accuracy in the valence classification task of music-induced emotion, reaching 94.85%, 98.41%, and 99.27%, respectively. The accuracy of the arousal classification task also gained 93.40%, 98.23%, and 99.20%, respectively. In addition, compared with the positive valence classification results of emotion, this method has obvious advantages in negative valence classification. This study provides a computational classification model for a music recommender system with emotion. It also provides some theoretical support for the brain-computer interactive (BCI) application products of Chinese ancient-style music which is popular among young people.

Application of Hybrid Filtering Strategies in Music Recommendation System

Everyone has their own distinct musical preferences; it's safe to assume that each music will find an appreciative audience. It's important to note that there isn't a single human society that has ever survived without music. There are two major gains from this study. Initially, a multi-strategy approach is taken to develop hybrid recommendation algorithms that give more accuracy than the existing algorithms. Also this hybrid algorithm is used to find new music in real time. This allows the algorithm to make an educated guess as to which musician and song best suit the user. As a second step, a general context-aware and emotion-based customized music framework is offered to facilitate the quick growth of context-aware music recommendation systems and to shed light on the whole recommendation procedure. Multiple methods exist for responding to requests, and a general framework is required for both collecting these methods and interpreting them within the context of the proposed framework. The kind of recommendation algorithm used is decided by the format of the input.

Neural content-aware collaborative filtering for cold-start music recommendation

State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the available listening data. These approaches inherently face the cold-start problem, as they cannot recommend novel songs with no listening history. Content-aware recommendation addresses this issue by incorporating content information about the songs on top of collaborative filtering. However, methods falling in this category rely on a shallow user/item interaction that originates from a matrix factorization framework. In this work, we introduce neural content-aware collaborative filtering, a unified framework which alleviates these limits, and extends the recently introduced neural collaborative filtering to its content-aware counterpart. This model leverages deep learning for both extracting content information from low-level acoustic features and for modeling the interaction between users and songs embeddings. The deep content feature extractor can either directly predict the item embedding, or serve as a regularization prior, yielding two variants (strict and relaxed) of our model. Experimental results show that the proposed method reaches state-of-the-art results for both warm- and cold-start music recommendation tasks. We notably observe that exploiting deep neural networks for learning refined user/item interactions outperforms approaches using a more simple interaction model in a content-aware framework.

Music Genre Classification Algorithm Based on Multihead Attention Mechanism

Retrieving music information is indispensable and divided into multiple genres. Music genres can be attributed to set categories, which are the indispensable functions of intelligent music recommendation systems. To improve the effect of music genre classification and model construction, combined with the music genre classification algorithm, this paper combines the multihead attention mechanism to study the music genre classification algorithm model, and it analyzes the key technology of music beamforming. Moreover, this paper has made a detailed description and derivation of the array antenna model, the principle of music beamforming, and the performance evaluation criteria of music adaptive beamforming. In the second half, the nonblind classical LMS algorithm, RLS algorithm, and variable step size LMS algorithm of adaptive beamforming are studied in detail. A music genre classification algorithm model based on the multihead attention mechanism is constructed. It can be seen from the experimental research that the music genre classification algorithm based on the multihead attention mechanism proposed in this paper has obvious advantages compared with the traditional algorithm, and it has a certain role in music genre classification.

Sistem Rekomendasi Musik dengan Metode Collaborative Filtering Berbasis Android

The r ecommendation system is a system that can suggest information based on the results of observation of users’ desires to users. In this study , the recommendation system can be implemented into an online music player application by displaying song recommendations so that the application looks more personal to its users. The research method used to design this music recommendation system is a collaborative filtering by which the music recommendations for users are determined . The system produce s a pretty good prediction when viewed from the MAE (Mean Absolute Error) score of 0.09639423292263861 and RMSE (Root Mean Squared Error) of 0.024737713540837314 , meaning that the smaller the evaluation result is or close to 0 , the more accurate it will be. The results of the MAE and RMSE calculations show that the prediction error rate is very small , so that they can be used as a parameter for determining music recommendations according to user s’ needs.

Fairness in Music Recommender Systems: A Stakeholder-Centered Mini Review.

The performance of recommender systems highly impacts both music streaming platform users and the artists providing music. As fairness is a fundamental value of human life, there is increasing pressure for these algorithmic decision-making processes to be fair as well. However, many factors make recommender systems prone to biases, resulting in unfair outcomes. Furthermore, several stakeholders are involved, who may all have distinct needs requiring different fairness considerations. While there is an increasing interest in research on recommender system fairness in general, the music domain has received relatively little attention. This mini review, therefore, outlines current literature on music recommender system fairness from the perspective of each relevant stakeholder and the stakeholders combined. For instance, various works address gender fairness: one line of research compares differences in recommendation quality across user gender groups, and another line focuses on the imbalanced representation of artist gender in the recommendations. In addition to gender, popularity bias is frequently addressed; yet, primarily from the user perspective and rarely addressing how it impacts the representation of artists. Overall, this narrative literature review shows that the large majority of works analyze the current situation of fairness in music recommender systems, whereas only a few works propose approaches to improve it. This is, thus, a promising direction for future research.

Music recommender system via deep learning

Music is an integral part of our everyday life. Almost everyone uses various streaming services to listen to music on their mobile phones. These streaming services recommend tailored music to each individual by categorizing music into numerous genres, thus enriching the user experience. The backbone of these streaming services is a music recommender system that recommends the best music to the users. The efficiency of these streaming services ultimately depends on the music recommender systems’ results. The music recommender system suggests music based on the user’s interests. The necessity of these music recommender systems is increasing day by day, but most of the systems do not meet users’ expectations. This paper plans and actualizes a general music recommender system that applies several audio signal features to train several machine learning and deep learning models for categorizing music into ten genres to meet the users’ expectations for music suggestions. The music recommender system consists of a feature extractor, a data preprocessing module, and a classifier. The system classifies the audio signals into blues, classical, country, disco, hip-hop, jazz, metal, pop, reggae, and rock. The classifiers such as support vector machine (SVM), K-nearest neighbors (KNN), random forest, and feedforward neural networks (FNN) are used for the system evaluation. The music classification results from our recommender show that the FNN based system has the highest prediction results than other classifiers. The FNN based music recommender system achieved 80% accuracy on the GTZAN music dataset and validates the impact of the deep learning models for music classification.

Music Recommendation System Using Facial Expression Recognition Using Machine Learning

Abstract: “MUSIC RECOMMENDATION SYSTEM USING FACIAL EXPRESSION RECOGNITION USING MACHINE LEARNING" is the title of this project. The study of human emotional responses to visual stimuli such as photos and films, known as visual sentiment analysis, has proven a fascinating and difficult problem. It attempts to comprehend the high-level information of visual data. The development of strong algorithms from computer vision is responsible for the present models' success. The majority of existing models attempt to overcome the problem by recommending either robust features or more sophisticated models. Visual elements from the entire image or video are the key proposed inputs in particular. Local areas have received less attention, which we believe is important to the emotional reaction of humans to the entire image. Image recognition is used to locate people in photos, analyse their emotions, and play emotion-related tunes based on their feelings. This repo accomplishes this goal by utilising the Google platform's Vision services. Given an image, it would look for faces, identify them, draw a rectangle around them, and describe the emotion it discovered

Improved Music Recommendation Algorithm for Deep Neural Network Based on Attention Mechanism

With the increasingly close combination of the Internet and people’s production and life, the total amount of global data and information also grows increasingly. In order to save users the time to find their favorite music among many music types, the music recommendation service arises at the historic moment and is widely concerned by scholars. Traditional music recommendation system based on the collaborative filtering algorithm has low recommendation accuracy, poor real-time performance, sparsity, system cold start, and so on. Moreover, the traditional music recommendation algorithm only simply uses user behavior characteristics and does not make good use of user history for listening to audio characteristics. In view of the above question, this section based on the attention mechanism of the deep neural network music recommendation algorithm, through the use of improved MFCC audio data preprocessing, the extracted audio combined with the user’s own portrait features, through the AIN RNN network recommended list, by learning user history listening to songs, improves the model-recommended accuracy.

Facial emotion recognition-based music recommendation system

Face recognition technology has gotten a lot of press because of its wide range of applications and market possibilities. It is used in a variety of fields, including security systems, digital video processing, and other technical advancements. Additionally, music is a form of art that is believed to have a stronger emotional connection It has a special power to improve one's mood. This research focuses on developing an effective music recommendation system that employs Facial Recognition algorithms to assess the user's sentiment. The constructed algorithm would prove to be more effective than previous systems. Furthermore, on a wider scale, this would allow for the recovery of time and labour spent physically performing the process. The system's overall goal is to recognise facial emotion and quickly recommend tunes. Both time and money will be saved with the proposed system.

Automatic Recommendation of Online Music Tracks Based on Deep Learning

It is one of the main goals of personalized music recommendation system that how to accurately recommend the songs in line with users’ interests in the huge music library. In view of the above problems, this study proposes a personalized music recommendation method based on convolutional neural network. First, this study defines a training set containing potential musical characteristics and, combined with the depth of the belief network, design a music information prediction model and the research in the music-type classification method with different dimensions. Based on selecting four different kinds of music information better describing the underlying characteristics of 40D feature vector to every song music composition, the music feature set is constructed. Then, the CNN (convolutional neural network), which is widely used in audio field, is used as the music information prediction model, and its structural parameters are redesigned to complete the multidimensional music information prediction, which solves the cold start problem to some extent.

Explainability in music recommender systems

AbstractThe most common way to listen to recorded music nowadays is via streaming platforms, which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of music recommender systems (MRSs) has become essential. Current real‐world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content‐based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate. While pure recommendation performance often correlates with user satisfaction, explainability has a positive impact on other factors such as trust and forgiveness, which are ultimately essential to maintain user loyalty.In this article, we discuss how explainability can be addressed in the context of MRSs. We provide perspectives on how explainability could improve music recommendation algorithms and enhance user experience. First, we review common dimensions and goals of recommenders explainability and in general of eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which these apply—or need to be adapted—to the specific characteristics of music consumption and recommendation. Then, we show how explainability components can be integrated within a MRS and in what form explanations can be provided. Since the evaluation of explanation quality is decoupled from pure accuracy‐based evaluation criteria, we also discuss requirements and strategies for evaluating explanations of music recommendations. Finally, we describe the current challenges for introducing explainability within a large‐scale industrial MRS and provide research perspectives.

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.