Music Recommendation System Research Articles

Dynamic Playlist Generation using Machine Learning

This study investigates the application of machine learning algorithms for dynamic playlist generation in the context of music recommendation systems. The traditional method of creating playlists frequently depends on immutable standards, like artist or genre, which might not adequately represent the unique and dynamic character of personal preferences. On the other hand, our suggested system makes use of machine learning methods to examine user behavior, preferences, and contextual elements in order to create playlists that are dynamically created and customized to each user’s individual preferences. In order to train machine learning models, the study collects user interaction data, such as listening history, skip patterns, and user feedback. To extract patterns and relationships from the data, a variety of algorithms are used, including hybrid models, content-based filtering, and collaborative filtering. After that, the models are incorporated into a dynamic playlist creation system that can eventually adjust to changing user preferences.Our test findings show how well the suggested strategy works to improve user experience by offering more interesting and customized playlists. With its ability to adjust to changing user preferences and contextual cues, the dynamic playlist generation system provides a smooth and pleasurable music discovery experience. We also talk about possible enhancements, implementation difficulties, and deployment considerations in the real world.This work adds to the ongoing efforts to improve music recommendation systems by demonstrating how machine learning can be used to develop more responsive and intelligent playlist generation systems. The results highlight how crucial customized experiences are in the constantly changing world of digital music consumption. Index Terms—hybrid mode, content-based filtering, intelligent playlist, music

Automated Determination of Music Genre and Linguistic Categorization

The main objective of the project is to build a system that allows users to predict genre as well as language out of some music . The Automated determination of musical genre and linguistic categorization is a machine learning based prediction model which is used to predict the genres and languages of an audio . Musical genres are categorical labels created by humans to characterize piece of music. A musical genre is characterized by common characteristics typically related to the instrumentation, rhythmic structure and harmonic content of the music. The performance and relative importance of the proposed features are investigated by training the statistical pattern recognition classifiers using real world audio collections and using machine learning algorithm.In this project, we built systems called music genre and language classification and the effects of different feature extraction methods and their various combinations were also investigated. This task is of great interest in music information retrieval and recommendation systems. In recent years, several machine learning algorithms have been developed to tackle this task, using features such spectral, temporal, and tonal characteristics of audio signals. Music genre prediction and language classification is a task that involves automatically predicting a piece of music into a specific genre category as well as language based on its audio features. Keywords-machine-learning,music,music genre and language classification

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.

Music Recommendation System by Detection of Emotions

Abstract: Expressing emotions through facial expressions is a fundamental aspect of human communication, offering insight into an individual's mood and well-being. The subtle nuances of the eyes, cheeks, forehead, and even the curve of a smile convey a wealth of emotional information. Music, as an art form, holds a unique power to relax and comfort both body and mind. Our project capitalizes on the synergy between these two elements by developing a system capable of discerning emotions from facial expressions and selecting appropriate music in response. This innovative approach not only aids in calming individuals and improving their mood but also streamlines the music selection process, saving valuable time. Furthermore, we aim to create software that offers this functionality universally, enabling users to access emotional music responses anytime, anywhere.

Harnessing Emotional Intelligence for Music Curation in Recommendation Systems

Abstract- When an individual decides the music he or she wants to listen to from a huge collection of already-existing options, sometimes it might get confusing. Diverse maneuvers have been executed for music, pride and food, and shopping based on user mood available, but there is no centrally managed network for all. The objective behind the technology of our music recommendation system is to give user-fitted recommendations to customers. The facial expression/user emotion analysis may a way to rectify defects in the current emotional or mental status of a user. When it comes to music and videos, this is the one area where there is a considerably large avenue for predictions based on customer tastes and previous records. This is a common thing that you've probably heard: that people often use facial expressions to be more direct about what they are really saying and the situation they are in. More than 60% of users agree with the sentiment that it will at some point become so difficult for them to determine which video they should play because quantity of videos in their library is really huge. Through the modelling of a robot dedicated to making recommendations about music to a user, it can facilitate the decision-making of which music one should be listening to and, thereby, lower the stress levels of the user. The user would not need to spend time on music searching or looking up suitable songs, as the best song paired with the user’s mood status would be successfully detected and immediately recommended to him/her by the application. The picture of the user is delivered through the camera incorporated into it. The picture of the user is taken, and then song is displayed on the screen in accordance with the mood or emotion of the user from their playlist, which matches the needs of the user.

Application of recommendation system based on fuzzy logic in music culture communication and music teaching management

Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals.

Una investigación sobre un sistema de recomendación musical basado en expresiones faciales mediante mecanismos de aprendizaje profundo

In this study, we propose a new music recommendation system (MRS) that combines facial expression recognition technology and deep learning algorithms to respond to the changing music industry environment and provide personalized music recommendations based on the user's emotional state. Our approach includes a thorough study of facial expression recognition, emotion-based music recommendation systems, and deep learning engines, as well as a detailed presentation of the MRS design, system architecture, and deep learning engines used. Through extensive experiments, we evaluate MRS's ability to accurately recognize facial expressions, filter music based on emotional states, and effectively recommend music to users. We analyze the results of follow-up experiments to identify the strengths and limitations of MRS compared to existing approaches, and conduct a comparative study with the latest music recommendation systems based on deep learning and emotion. This comparison highlights the originality and potential of the proposed MRS system to improve user experience and promote the development of artificial intelligence-based music recommendation systems. This study demonstrates the problem of accurately determining a user's emotional state from facial expressions, which requires the integration of facial expression recognition systems, deep learning, and music recommendation systems. Using advanced deep learning techniques and a comprehensive experimental setup, the proposed MRS provides a solution to this problem by facilitating accurate emotional state identification and personalized music recommendations. Overall, MRS represents a powerful and innovative response to the growing demand for accurate and reliable music recommendations and shows significant potential for future collaboration and development of AI-based music recommendation systems.

Hybrid Deep Learning Approach to Emotion-Infused Music Recommendation

Music holds a crucial place in our daily lives, uplifting our spirits and contributing to our overall well-being. However, not all musical genres are suitable for every emotional state, and the vast digital music repositories make it challenging to pinpoint the perfect tune for a specific mood. With the ever-expanding song options, individuals often face confusion when selecting tracks. To address this, a context-aware music recommendation system is introduced, designed to recognize users' current emotions and suggest music that aligns with those feelings. This system takes a comprehensive approach, integrating both context and emotion elements to enhance user preference prediction. The overarching goal is to simplify the music selection process, providing users with a more seamless, intuitive, and enjoyable listening experience. The forthcoming evaluation will delve into performance metrics and research findings, contributing to the ongoing refinement and optimization of this context-sensitive music recommendation strategy. Keywords: Emotion Detection, CNN(Convolutional Neural Network),Video and audio music recommendation

A Survey on Speech Emotion based Music Recommendation System

Psychological research has proven that music can reduce stress, improve mood, and help release “happy” chemicals like oxytocin, serotonin, and dopamine. It's no surprise that music has always been a popular tool in clinics and in the treatment of many ailments, so as people facing mental health issues continue to wander around the world, coping with mental illness is more important than ever. Although music recommendations already exist, they do not have all the scheduling algorithms that take into account the user's needs. Considering that it is undeniable that most people listen to music on a regular basis, music has also been shown to reduce stress, pain, and blood pressure while improving cognition, memory, and sleep quality. The most comprehensive and practical product that sorts all the good music. Our goal is to improve the emotional state of the user by creating a positive feeling based on the recommended music called "Viby".

MMD-MII Model: A Multilayered Analysis and Multimodal Integration Interaction Approach Revolutionizing Music Emotion Classification

Music plays a vital role in human culture and society, serving as a universal form of expression. However, accurately classifying music emotions remains challenging due to the intricate nature of emotional expressions in music and the integration of diverse data sources. To address these challenges, we propose the Multilayered Music Decomposition and Multimodal Integration Interaction (MMD-MII) model. This model employs cross-processing to facilitate interaction between audio and lyrics, ensuring coherence in emotional representation. Additionally, we introduce a hierarchical framework based on the music theory, focusing on the main and chorus sections, with the chorus processed separately to extract precise emotional representations. Experimental results on the DEAM and FMA datasets demonstrate the effectiveness of the MMD-MII model, achieving accuracies of 49.68% and 49.54% respectively. Compared with the existing methods, our model outperforms in accuracy and F1 scores, offering promising implications for music recommendation systems, healthcare, psychology, and advertising, where accurate emotional analysis is essential.

Literature Review : Emotion-Based Music Recommendation System with Age Detection

This research introduces an innovative Emotion-Based Music Recommendation System coupled with Age Estimation. Leveraging computer vision techniques, specifically Convolutional Neural Networks (CNNs), the proposed system analyzes facial data, obtained either through a camera or OpenCV, to discern both the emotional state and age of an individual. The synergy of emotion and age information serves as the foundation for personalized music recommendations. To compose playlists and propose songs, a third-party API is used, personalizing the musical experience to the user's emotional state and age group. This integration of facial analysis, deep learning, and music recommendation aims to enhance user engagement and satisfaction by providing a more contextually relevant and emotionally resonant music selection. Keywords— Emotion-Based Music Recommendation System, Age Estimation, Convolutional Neural Networks (CNN), OpenCV, Deep Learning, Third-Party API.

A Survey on Speech Emotion Based Music Recommendation System

Psychological research has proven that music relieves stress, elevates mood, and is responsible for the release of “feel-good” chemicals like oxytocin, serotonin, and dopamine. It comes as no surprise that music has been a popular tool in rehabilitation centers and therapy for various disorders, thus with the interminably rising numbers of people facing mental health-related issues across the globe, addressing mental health concerns is more crucial than ever. Despite the existing music recommendation systems, there is a dearth of holistically curated algorithms that take care of the needs of users. Given that, an undeniable majority of people turn to music on a regular basis and that music has been proven to increase cognition, memory, and sleep quality while reducing anxiety, pain, and blood pressure, it is the need of the hour to fashion a product that extracts all the benefits of music in the most extensive and deployable method possible. Our project aims to ameliorate our users’ mental state by building a comprehensive mood-based music recommendation system called “Viby”. Keywords Language, Communication, Speech recognition, Interaction.

Music Recommendation System based on Facial Expressions by using CNN

Abstract: In today's world, the integration of emotion detection and personalized music recommendation has become increasingly prevalent across diverse fields. This project endeavors to develop a cutting-edge music recommendation system that tailors playlists to users' emotional states, as discerned from their facial expressions in real-time. Leveraging the Facial Expression Recognition 2013 (FER 2013) dataset, which encompasses a wide array of emotional expressions including happiness, sadness, anger, fear, disgust, neutrality, and surprise, serves as the foundation for training our model. The project employs the Mediapipe algorithm for feature extraction from facial expressions and utilizes a Convolutional Neural Network (CNN) to classify these emotions accurately. For seamless integration and user interaction, streamlit web browser frameworks are harnessed to facilitate music recommendations within an intuitive interface. Furthermore, the proposed system capitalizes on real-time face detection through camera input using OpenCV, ensuring timely and responsive interaction. By automatically generating music playlists based on the user's current emotional state, our system aims to significantly reduce computational time and overall costs compared to existing literature. In essence, this project presents a comprehensive solution that combines real-time facial expression analysis with personalized music recommendation, offering enhanced efficiency and accuracy while minimizing computational overhead.

Music Genre Classification Using K-Nearest Neighbor and Mel-Frequency Cepstral Coefficients

Music genre classification plays a pivotal role in organizing and accessing vast music collections, enhancing user experiences, and enabling efficient music recommendation systems. This study focuses on employing the K-Nearest Neighbors (KNN) algorithm in conjunction with Mel-Frequency Cepstral Coefficients (MFCCs) for accurate music genre classification. MFCCs extract essential spectral features from audio signals, which serve as robust representations of music characteristics. The proposed approach achieves a commendable classification accuracy of 80%, showcasing the effectiveness of KNN-MFCC fusion. Nevertheless, the challenge of overlapping genres, particularly rock and country, demands special attention due to their shared acoustic attributes. The inherent similarities between these genres often lead to misclassification, hampering accuracy. To address this issue, an enhanced feature engineering strategy is devised, leveraging deeper insights into the subtle nuances that differentiate rock and country music. Additionally, a refined KNN distance metric and neighbor selection mechanism are introduced to further refine classification decisions. Experimental results underscore the effectiveness of the refined approach in mitigating genre overlap issues, significantly enhancing classification accuracy for rock and country genres. This study contributes to the advancement of music genre classification techniques, offering an innovative solution for handling overlapping genres and demonstrating the potential of KNN-MFCC synergy in achieving accurate and refined genre classification.

Investigating the working principle of the recommending system and designing a personal music recommending system

This paper, Investigating the Working Principle of the Recommending System and Designing a Personal Music Recommending System, delves into the intricacies of recommendation systems, with a specific focus on music. It begins by exploring the fundamental principles that underpin recommendation systems.The latter part of the paper presents the design of a novel personal music recommendation system. This system leverages the results of K-means clustering to provide faster personalized music recommendations. It considers various auditory factors such as valence (the musical positiveness conveyed by a track), duration (the length of the track), and energy (a measure from 0.0 to 1.0 and represents a perceptual measure of intensity and activity). These factors are calculated by Spotify and made available in an open data set. This research contributes significantly to the field by providing a deeper understanding of recommendation systems and proposing an innovative solution for personalized music recommendations. The novelty of this system lies in its use of K-means clustering and the specific auditory factors it considers, which sets it apart from existing systems.

Design of music recommendation system based on EDA and K-means cluster analysis

This paper describes the construction and analysis of a music recommendation system, through visual and exploratory analysis of the open source dataset of music recommendation system in the UCL database, exploring the relationship between music popularity and music acoustic properties, using the K-means clustering algorithm for clustering analysis of genres and songs, and finally building a music recommendation system that can, based on the user input of music data, to match the ten music songs that best meet the listener's taste. The analyses in this paper show that there is a certain correlation between music popularity and the indicators of sound, danceability, energy, instrumentality, liveliness, loudness, language, rhythm, mood, duration and tone. The indicators of energy, liveliness, loudness and tempo are positively correlated with the popularity of music, while the indicators of mood and duration are negatively correlated with the popularity of music. In addition, this paper uses K-means clustering algorithm to cluster analysis of genres and songs, and shows the similarity and difference between different genres and songs through visualisation, which provides important data support for the construction of music recommendation system. Finally, this paper establishes a music recommendation system based on K-means clustering algorithm, which can match the ten music songs that best meet the listener's taste according to the music data input by the user. The system is intelligent and personalised, and can recommend music works that are more in line with the user's taste based on the user's preferences and historical listening records. The results of this paper provide useful reference and support for the algorithms and models of the music recommendation system, which can be further researched in depth in the future to improve its intelligence and personalisation level and provide users with better music services.

Music Recommendation System Based on Emotion Detection

Emotion of an individual can be identified by their facial expression. Facial expression can be determined from human face directly using camera. The data can then be used to get a list of songs that is derived from their mood. Music Recommendation System Based on Emotion Detection aims to create a playlist based on the facial expression. The system focuses on generating a playlist based on the facial expression that helps to eliminate the time consumed in finding the song user wants to play.

Emotion-Driven Soundtrack Selector using AI

Abstract: The work portrayed the advancement of emotion-based Music Player, which is a web application implied for a wide range of clients, explicitly the music sweethearts. With the convenience of music players and other streaming apps, people can listen to music anytime, anywhere, and engage in various activities such as traveling, sports, or daily routines. The advancement of mobile networks and digital multimedia technologies has made digital music the most sought-after consumer content by young people. Moreover, listening to the appropriate music at the right time has the potential to enhance mental well-being. Human inclination assumes an indispensable part as of late. Emotion depends on human sentiments which can be both communicated or not. Emotion communicates the human's singular way of behaving which can be in various forms. The target of this project is to separate elements from the human face and recognize the emotion and play music as per the emotion identified. Be that as it may, many existing/strategies utilize past information to recommend music and different calculations utilized no Facial articulations are caught by a nearby catching gadget or an inbuilt web camera. Here we utilize a calculation for the acknowledgment of the component from the emotion we caught. Many music recommendation systems use content-based or collaborative-based recommendations. However, the choice of music for a user is not only based on his historical preference of the user. But also based on the mood of that user. This project proposes an emotion-based music recommendation that detects the emotion of a user from the webcam and plays the songs based on the user’s mood. In particular, the emotion of a user is classified and predicted the emotion and automatically redirects to the music page. This emotional information is stored in welldefined arrays. Thus, the model can predict the emotion and accuracy can be increased using these data.

Social Network Community Detection to Deal with Gray-Sheep and Cold-Start Problems in Music Recommender Systems

Information from social networks is currently being widely used in many application domains, although in the music recommendation area, its use is less common because of the limited availability of social data. However, most streaming platforms allow for establishing relationships between users that can be leveraged to address some drawbacks of recommender systems. In this work, we take advantage of the social network structure to improve recommendations for users with unusual preferences and new users, thus dealing with the gray-sheep and cold-start problems, respectively. Since collaborative filtering methods base the recommendations for a given user on the preferences of his/her most similar users, the scarcity of users with similar tastes to the gray-sheep users and the unawareness of the preferences of the new users usually lead to bad recommendations. These general problems of recommender systems are worsened in the music domain, where the popularity bias drawback is also present. In order to address these problems, we propose a user similarity metric based on the network structure as well as on user ratings. This metric significantly improves the recommendation reliability in those scenarios by capturing both homophily effects in implicit communities of users in the network and user similarity in terms of preferences.

Movie and Music Recommendation System based on Facial Expressions

Abstract: This paper depicts a novel approach of recommendation system created to extract user’s facial expressions and analyze it using Convolutional Neural Network. CNN is a type of artificial neural network which primarily is used for image recognition and processing by recognizing patterns in images. Initially, an inbuilt webcam will operate to detect user’s facial expression which reduces system’s cost compared to alternative methods. The main concern in existing recommendation systems is manual sorting, so to avoid it, this model is proposed. ‘Movie and Music Recommendation System based on Facial expressions’ provides a way to automatically play the music and movie without spending much time browsing it. The most common problem we all face is, whenever we hop onto third party movies and music platforms, we find tons of options and get stuck into the confusion of what to watch in terms of movies and spend half of our time surfing the options and then we decide to watch something specific. Computer vision is used to understand people in images and videos. This research project introduces an ingenious recommendation system that capitalizes on facial expressions in real time, customize movie and music suggestions through emotion analysis. Haar Cascade algorithm and CNN targets to detect facial feature detection. The database of movies is routed to IMDB site and songs to Spotify API. Training of dataset is done on FER 2013 sample images. It has an input layer, convolutional layer, dense layer and output layer. After the emotions are detected, mood classification is done (for song: tone, valence, timber; for movie: happy, surprise, sad, neutral, anger.) The model is displayed on a website created using Html and CSS with Flask as interface. This system yields better performance results. This recommendation system is exhibited on a website which is user friendly created with HTML and CSS with Flask.