Music Recommendation System Research Articles

A Personalized Multimodal Approach to Music Recommendation for Music Therapy

Mental health disorders and suicidality are rising among adolescents and young adults (A-YA) while rates of treatment engagement continue to remain low. Though the effectiveness of music therapy to enhance mental health is yet unknown for A-YA, the power of music to evoke emotions, facilitate communication, and promote relaxation suggests its promise. In this research study we discuss a multimodal music recommendation system which is capable of taking inputs in the form of a BFI-10 questionnaire, a series of self-view questions in the form of a survey, and an image of the user’s face in order to determine music preferences based on existing correlations between personality traits and taste in music, as an effort to make advancements in the field of music therapy.

Chatbot: Music Recommendation System

This paper presents the development of a Chatbot Music Recommendation System that utilizes artificial intelligence to enhance user engagement through personalized music suggestions. The system operates on two primary mechanisms: passive recommendations based on contextual factors such as time of day and weather conditions, and interactive suggestions during user engagement. By integrating real-time data from external APIs, the chatbot can provide relevant song recommendations tailored to the user's environment, such as upbeat tracks for sunny days or calming melodies for rainy weather. Additionally, the system employs natural language processing techniques to understand user preferences and emotions, allowing it to refine its recommendations over time. A comprehensive evaluation of the system's performance is conducted through user feedback and interaction metrics, demonstrating its effectiveness in improving the music discovery experience. The findings indicate that the chatbot not only enhances user satisfaction but also fosters a deeper emotional connection with music. This research contributes to the field of music recommendation systems by merging technology with user-centric design, ultimately transforming how individuals interact with music in their daily lives

Brainwave for gender-based music recommendation system analysis

Brainwave for gender-based music recommendation system analysis

Music source feature extraction based on improved attention mechanism and phase feature

Music source feature extraction is an important research direction in music information retrieval and music recommendation system. To extract the features of music sources more effectively, the study introduces the jump attention mechanism and combines it with the convolutional attention module. Also, a feature extraction module based on Unet + + and spatial attention module is proposed. In addition, the phase feature information of the mixed music signals is utilized to improve the network performance. Results showed that this model was studied to perform well in music source separation experiments of vocals and accompaniment. For vocal separation on the MIR-1K dataset, the model achieves 11.25 dB, 17.34 dB, and 13.83 dB for each metric, respectively. Meanwhile, for drum separation on the DSD100 dataset, the model achieves a median signal-to-source distortion ratio of 4.36 dB, which is 2.91 dB better than that of the Spectral Hierarchical Network model. For the separation of the bass sound and the human voice, the model's in the separation of bass and human voice, the median distortion ratio of the model is as high as 4.87 dB and 6.09 dB, which is better than that of the Spectral Hierarchical Network model. This indicates the significant performance advantages in feature extraction and separation of music sources, and it has important application values in music production and speech recognition.

Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques

Music Information Retrieval (MIR) has become a pivotal area of research with the rise of digital music platforms, enabling personalized music recommendations to enhance user experience. This paper explores the integration of machine learning and data mining techniques in music recommendation systems. We discuss user-based and item-based collaborative filtering, matrix factorization methods like Singular Value Decomposition (SVD) and Alternating Least Squares (ALS), and content-based filtering that incorporates audio feature analysis, metadata, and lyrics analysis. Additionally, we delve into hybrid recommendation systems, combining collaborative and content-based approaches using advanced models such as neural networks and hybrid autoencoders. Our finding show that, hybrid systems provide the most accurate and personalize recommendations, albeit requiring significant computational resources. Practical applications from platforms likes Spotify and Pandora illustrate the effectiveness of these approaches in real-world settings.

A GAI-based multi-scale convolution and attention mechanism model for music emotion recognition and recommendation from physiological data

Due to the subjectivity of emotions and the limited number of emotion categories, existing deep learning models require assistance to achieve objective, accurate, and flexible personalized music emotion recommendations. This paper introduces a deep learning approach that combines Generative Artificial Intelligence (GAI) and explicitly leverages physiological indicators to enhance the model's intelligence, versatility, and automation. Physiological indicators such as Heart Rate Variability (HRV) and Galvanic Skin Response (GSR) can be measured using sensors placed on the body's surface, providing more precise information about human emotional changes. This research employs a three-dimensional emotion model, including the tension-arousal axis, energy-arousal axis, and valence axis, to explain the correlation and accuracy between music data and emotions. Based on this, a music emotion classifier is designed, incorporating GAI algorithms to recommend music by matching users' physiological and emotional types with the emotional features of music. The classifier uses Mel-Frequency Cepstral Coefficients (MFCC) to transform audio into Mel-spectrogram as input features. The music emotion selection module adopts a GAI framework of Variational Autoencoder (VAE) and integrates multi-scale parallel convolution and attention mechanism modules. Experimental results demonstrate that this approach is competitive compared to existing deep learning architectures on PMEmo, RAVDESS, and Soundtrack datasets. Furthermore, due to GAI's efficient classification capability, this model is suitable for resource-constrained mobile devices and other smart devices. The results of this study can be applied to emotion-based music recommendation systems, contributing to emotional interventions and improving the performance of exercise and music therapy.

Analysis of machine learning-based music recommendation system using Spotify datasets

The field of musical exploration grows rapidly with the rapid development of technology in the digital era. In this paper, the application of machine learning in music recommendation systems is mainly focused on and the existing data sets about Spotify are used to analyze and show how machine learning achieves musical personality recommendations. This paper mainly uses principal component analysis and the K-means clustering algorithm to realize the goal. Principal component analysis (PCA) helps people to reduce the dimensionality of the whole dataset, and this method helps people to retain key information and reduce the complexity of the data. The k-means clustering algorithm divides the songs into different clusters. These clusters show consistency in musical features, and the dots will cluster together. By visualization of the clusters, people can deeply interpret the relationship between distinct music genres and discover the areas of music and songs that users are interested in. This research not only explains how music exploration is implemented but also promotes the development of personalized music recommendation systems.

Emotion Based Music Recommendation System using VGG16-CNN Architecture

Abstract: This research introduces an Emotion-based Music Recommendation System (EMRS) using Convolutional Neural Networks (CNNs) to analyze facial expressions and recommend music tailored to individual emotional states. Unlike traditional systems, EMRS prioritizes facial expression analysis for personalization. CNNs, trained on a diverse dataset of emotional expressions linked to music, extract key emotional features. EMRS leverages this analysis to intuitively suggest music that can potentially aid in emotional regulation. This research not only advances personalized music recommendation but also opens doors for emotion-aware technology with applications in mental healthcare for emotional imbalance and trauma. EMRS has the potential to serve as a complementary tool for therapists and individuals seeking emotional well-being through music

EMOTION BASED MUSIC RECOMMEDENTION SYSTEM

Abstract: Emotion-based music recommendation is a challenging task that has attracted significant research attention in recent years. This paper proposes a novel approach to emotion-based music recommendation using Support Vector Machines (SVMs). Our approach uses images as inputs instead of pronouns to capture the emotional state of the user more accurately. We collected a dataset of images associated with different emotional states and extracted features using Convolutional Neural Networks (CNNs). Then, we trained an SVM model to predict the emotional state of the user based on the input image. Finally, we used the output of the SVM model to recommend music tracks that are most suitable for the user’s emotional state. Our experiments on a large dataset show that our approach outperforms existing methods in terms of accuracy and user satisfaction. The proposed approach provides a promising direction for emotion-based music recommendation systems that can enhance the user experience by providing personalized music recommendations based on their emotional state. Keywords- Emotion Detection, Face Recognition, Music API.

Facial Emotion Recognition System Using CNN for Song Mapping

This project introduces a novel system that integrates Facial Emotion Recognition (FER) with music mapping to enhance human-computer interaction. Leveraging a custom Convolutional Neural Network (CNN) algorithm, developed using the FER2013 dataset, we classify emotions into four categories- happy, sad, neutral, and angry- employing Haar Cascade for precise face detection and grayscale conversion for optimal CNN input. Our custom CNN demonstrates superior performance, achieving a testing accuracy of 77.23%, notably surpassing established models like VGG16 and ResNet50, which achieved 55.87% and 62.76% respectively. This system swiftly identifies emotions and recommends songs from the "278k Emotion Labeled Spotify Songs" playlist, aiming to boost user satisfaction. Through nuanced comparisons with VGG16 and ResNet50, our approach underscores its inherent strengths, suggesting promising advancements in Facial Emotion Recognition (FER) and music recommendation systems. With a focus on precision in emotion detection, our system subtly elevates user experience, contributing meaningfully to ongoing research in the field.

A music recommender system based on compact convolutional transformers

In recent years, music streaming services have garnered a surge in popularity. Maintaining a continuous flow of music that aligns with user preferences, commonly known as the continuity problem, has become a significant issue in this domain. To address this challenge, developing Music Recommender Systems (MRSs) that can automatically search through vast music libraries and suggest appropriate songs to listeners is crucial. To this end, this paper proposes a Compact Convolutional Transformer (CCT) model for improving the feature selection process and thus addressing the continuity problem based on music genres. The model extracts latent features from Mel-spectrograms generated from raw audio songs. Then, the cosine similarity measure determines the similarity between feature maps to recommend the most relevant songs. Several methodologies, including two state-of-the-art CRNN models, are used to benchmark the model’s performance. The experimental results demonstrate that the proposed model significantly outperforms the current state-of-the-art models in terms of precision, recall, F1 score, and overall accuracy while having significantly fewer parameters.

Applying Multi-Armed Bandit algorithms for music recommendations at Spotify

This study explores the application of multi-armed bandit algorithms in enhancing music recommendation systems, with a focus on Spotify. It delves into the Explore-Then-Commit (ETC), Upper Confidence Bound (UCB), and Thompson Sampling (TS) algorithms, evaluating their efficacy within the Spotify context. The primary objective is to determine which algorithm optimally balances exploration and exploitation to maximize user satisfaction and engagement. The research reveals that the ETC algorithm, with its rigid exploration and exploitation phases, incurs a notably higher regret value. This rigidity can lead to missed opportunities in identifying optimal choices and hinder adaptability to evolving user preferences. Conversely, the UCB and TS algorithms exhibit remarkable adaptability and a flexible balance between exploration and exploitation. This flexibility translates into more personalized and satisfactory user experiences in music recommendations. However, the selection of the most appropriate algorithm should be contingent on the size and characteristics of the specific user dataset, as well as the fine-tuning of algorithm parameters to align with user preferences and behaviors.

A novel similarity measure SF-IPF for CBKNN with implicit feedback data

PurposeThe increasing popularity of music streaming services also increases the need to customize the services for each user to attract and retain customers. Most of the music streaming services will not have explicit ratings for songs; they will have only implicit feedback data, i.e user listening history. For efficient music recommendation, the preferences of the users have to be infered, which is a challenging task.Design/methodology/approachPreferences of the users can be identified from the users' listening history. In this paper, a hybrid music recommendation system is proposed that infers features from user's implicit feedback and uses the hybrid of content-based and collaborative filtering method to recommend songs. A Content Boosted K-Nearest Neighbours (CBKNN) filtering technique was proposed, which used the users' listening history, popularity of songs, song features, and songs of similar interested users for recommending songs. The song features are taken as content features. Song Frequency–Inverse Popularity Frequency (SF-IPF) metric is proposed to find the similarity among the neighbours in collaborative filtering. Million Song Dataset and Echo Nest Taste Profile Subset are used as data sets.FindingsThe proposed CBKNN technique with SF-IPF similarity measure to identify similar interest neighbours performs better than other machine learning techniques like linear regression, decision trees, random forest, support vector machines, XGboost and Adaboost. The performance of proposed SF-IPF was tested with other similarity metrics like Pearson and Cosine similarity measures, in which SF-IPF results in better performance.Originality/valueThis method was devised to infer the user preferences from the implicit feedback data and it is converted as rating preferences. The importance of adding content features with collaborative information is analysed in hybrid filtering. A new similarity metric SF-IPF is formulated to identify the similarity between the users in collaborative filtering.

Bangla song genre recognition using artificial neural network

<p>Music has a control over human moods and it can make someone calm or excited. It allows us to feel all emotions we experience. Nowadays, people are often attached with their phones and computers listening to music on Spotify, Soundcloud or any other internet platform. Music Information retrieval plays an important role for music recommendation according to lyrics, pitch, pattern of choices, and genre. In this study, we have tried to recognize the music genre for a better music recommendation system. We have collected an amount of 1820 Bangla songs from six different genres including Adhunik, Rock, Hip hop, Nazrul, Rabindra and Folk music. We have started with some traditional machine learning algorithms having K-Nearest Neighbor, Logistic Regression, Random Forest, Support Vector Machine and Decision Tree but ended up with a deep learning algorithm named Artificial Neural Network with an accuracy of 78% for recognizing music genres from six different genres. All mentioned algorithms are experimented with transformed mel-spectrograms and Mean Chroma Frequency Values of that raw amplitude data. But we found that music Tempo having Beats per Minute value with two previous features present better accuracy.</p>

Performance Analysis of Deep Learning and Machine Learning Methods for Music Genre Classification System

Classification plays a crucial role in numerous applications within the music industry, spanning from content management to personalized playlists and music recommendation systems. While previous research has explored various machine learning frameworks for this purpose, such as support vector machines (SVM), a comprehensive comparison analysis of convolutional neural networks (CNN) and k-nearest neighbor (KNN) remains unknown. This study aims to address this gap by analyzing and contrasting the performance of SVM, KNN, and CNN in music genre classification. Each algorithm was carefully trained using different music genres from a protected dataset, employing feature extraction methods to capture the appropriate qualities of audio signals. The models underwent extensive training with a limited number of samples, and their performance was evaluated using industry standards such as accuracy, precision, recall, and F1 scores. Experimental results included SVM, KNN, and CNN for music genre designation. This study contributes significantly to existing literature by providing a comparative analysis of these algorithms. The findings highlight the strengths and limitations of each approach, offering guidance for researchers and practitioners in choosing the most suitable approach for their specific needs. The insights gained from this research have the potential to enhance music genre classification systems, ultimately improving the user experience across various music-related contexts.

Music Beats - A Music Recommendation System using Facial Expressions

Abstract: Interest in the nexus between music recommendation systems and affective computing has grown recently. This article examines the state of the art in music recommendation systems that use facial expression analysis to improve customer satisfaction. Facial expression detection has been incorporated into computer vision and machine learning systems, opening up new possibilities for personalized music curating. The basic theories and techniques supporting facial expression analysis in the context of emotional computing are first examined in this study. It explores the developments in emotion identification technology, emphasizing important datasets, algorithms, and their advantages and disadvantages. Additionally, this research assesses how well these systems capture user preferences while resolving issues with scalability, accuracy, and privacy. It talks about the effects on user satisfaction and engagement of using facial expressions as a channel for feedback in recommendation systems. In order to improve and develop facial expression-based music recommendation systems, this paper concludes by outlining future directions and possible research routes and highlighting the necessity of multidisciplinary cooperation between computer science, psychology, and musicology. To sum up, this paper offers a thorough analysis of the current state-of-the-art in facial expression-driven music recommendation systems, outlining their innovations, difficulties, and potential directions for growth of multidisciplinary cooperation between computer science, psychology, and musicology

Classical music recommendation algorithm on art market audience expansion under deep learning

Abstract The purpose of the study is to help users know about their favorite music and expand art market audiences. First, the personalized recommendation data of classical music are obtained based on the deep learning recommendation algorithm technology, artificial intelligence, and music playback software of users. Second, a systematic experiment is conducted on the improved recommendation algorithm, and a classical music dataset is established and used for model training and user testing. Then, the network model of the classical music recommendation algorithm is constructed through the typical convolutional neural network model, and the optimal parameters suitable for the model are found. The experimental results show that the optimal value of the dimension in the hidden layer is 192, and 24,000 training rounds can converge to the global optimum when the learning rate is 0.001. The personalized recommendation is provided for target users by calculating the similarity between user preference and potential features of classical music, relieving the auditory fatigue of art market audiences, improving user experience, and expanding the art market audience through the classical music recommendation system.

Socially-Motivated Music Recommendation

Extensive literature spanning psychology, sociology, and musicology has sought to understand the motivations for why people listen to music, including both individually and socially motivated reasons. Music's social functions, while present throughout the world, may be particularly important in collectivist societies, but music recommender systems generally target individualistic functions of music listening. In this study, we explore how a recommender system focused on social motivations for music listening might work by addressing a particular motivation: the desire to listen to music that is trending in one’s community. We frame a recommendation task suited to this desire and propose a corresponding evaluation metric to address the timeliness of recommendations. Using listening data from Spotify, we construct a simple, heuristic-based approach to introduce and explore this recommendation task. Analyzing the effectiveness of this approach, we discuss what we believe is an overlooked trade-off between the precision and timeliness of recommendations, as well as considerations for modeling users' musical communities. Finally, we highlight key cultural differences in the effectiveness of this approach, underscoring the importance of incorporating a diverse cultural perspective in the development and evaluation of recommender systems.

Predictive Music Based on Mood

It is often difficult for a person to choose which mu- sic to listen to from a vast array of available options. Relatively, this paper focuses on building an efficient music recommendation system based on the user’s mood which determines the emotion of user using Facial Recognition technique. The model is build using the transfer learning approach for which MobileNet model and Cascade classifier are used. Analyzing the user’s face expression might help you better comprehend their current emotional or mental condition. Music and video are one area where there is a lot of potential to present clients with a variety of options depending on their interests and data. More than 60% of users anticipate that the number of songs in their music collection will grow to the point where they will be unable to find the song they need to play at some point in the future. The user would save time by not having to search for or look up tunes. The image of the user is captured using a webcam. Then, depending on the user’s mood, an appropriate song from the user’s playlist or a movie is shown.

A cutting-edge artificial intelligence paradigm for entertainment-infused music recommendations

Personalized and fascinating music recommendations are becoming increasingly in demand as the advent of technology continues to change the way that people consume music. Traditional music recommendation systems primarily rely on user listening history and preferences, often neglecting the emotional and experiential dimensions that make music a deeply personal and entertaining endeavour. This research introduces a music recommendation system after classifying the tracks using a novel Gravitational Search Optimized Recursive Neural Networks (GS-RNN) approach. GS-RNN addressed this gap by integrating the Gravitational Search Algorithm (GSA) with Recursive Neural Networks (RNN) to create a content-based recommendation system that assesses audio signal similarity. Evaluation metrics, including accuracy (80%), logarithmic loss (0.85), precision (84%), recall (83%), and F1-score (88%), demonstrate GS-RNN’s superiority over existing techniques. Genre-specific accuracy analysis further underscores the model’s capability to suggest songs within the same genre. Overall, GS-RNN presents a novel and effective paradigm for music recommendations.