Music Recommendation Research Articles

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.

Enhancing user experience: a content-based recommendation approach for addressing cold start in music recommendation

Enhancing user experience: a content-based recommendation approach for addressing cold start in music recommendation

Hybrid music recommendation with graph neural networks

Hybrid music recommendation with graph neural networks

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.

Double music recommendation algorithm based on multi-label propagation hierarchical clustering analysis

Double music recommendation algorithm based on multi-label propagation hierarchical clustering analysis

FFA-BiGRU: Attention-Based Spatial-Temporal Feature Extraction Model for Music Emotion Classification

Music emotion recognition is becoming an important research direction due to its great significance for music information retrieval, music recommendation, and so on. In the task of music emotion recognition, the key to achieving accurate emotion recognition lies in how to extract the affect-salient features fully. In this paper, we propose an end-to-end spatial-temporal feature extraction method named FFA-BiGRU for music emotion classification. Taking the log Mel-spectrogram of music audio as the input, this method employs an attention-based convolutional residual module named FFA, which serves as a spatial feature learning module to obtain multi-scale spatial features. In the FFA module, three group architecture blocks extract multi-level spatial features, each of which consists of a stack of multiple channel-spatial attention-based residual blocks. Then, the output features from FFA are fed into the bidirectional gated recurrent units (BiGRU) module to capture the temporal features of music further. In order to make full use of the extracted spatial and temporal features, the output feature maps of FFA and those of the BiGRU are concatenated in the channel dimension. Finally, the concatenated features are passed through fully connected layers to predict the emotion classification results. The experimental results of the EMOPIA dataset show that the proposed model achieves better classification accuracy than the existing baselines. Meanwhile, the ablation experiments also demonstrate the effectiveness of each part of the proposed method.

Research and Application of a Dual Filtering Music Hybrid Recommendation Model Based on Cat Boost Algorithm and DCN

Research and Application of a Dual Filtering Music Hybrid Recommendation Model Based on Cat Boost Algorithm and DCN

Enhancing Music Mood Recognition with LLMs and Audio Signal Processing: A Multimodal Approach

Abstract: Music Mood Recognition aims to allow computers to understand the emotions behind music the way humans do, in order to facilitate better perception of media by computers to aid in enhanced services like music recommendations, therapeutic interventions, and Human Computer Interaction. In this paper, we propose a novel approach to improving Music Mood Recognition using a multi-modal model that uses lyrical and audio features of a song. Lyrical features are analysed using stateof-the-art open-source Large Language Models like Microsoft Phi-3 to classify lyrics from one of the four possible emotion categories as per the James Russel Circumplex Model. Audio features are used to train a Deep Learning (ConvNet) model to predict emotion classes. A multimodal combiner model with Audio and Lyrics is then trained and deployed to enable accurate predictions. The dataset used in this research is “MoodyLyrics”, a collection of 2000+ songs classified with one of 4 possible emotion classes as per the James Russel Circumplex Model. Due to compute limitations, we are using a balanced set of 1000 songs to train and test our models. The workin this paper outperforms most other multimodal researches by allowing higher accuracies with universal language support

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.

FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel approaches to quantify and enforce fairness. Indeed, typical recommender systems recommend each user their top relevant items, such that desirable items may be recommended simultaneously to more and to less qualified individuals. This is arguably unfair to the latter. Indeed, when they pursue such a desirable recommendation (e.g., by applying for a job), they are unlikely to be successful. To quantify fairness in such settings, we introduce inferiority : a novel (un)fairness measure that quantifies the competitive disadvantage of a user for their recommended items. Inferiority is complementary to envy : a previously-proposed fairness notion that quantifies the extent to which a user prefers other users’ recommendations over their own. We propose to use both inferiority and envy in combination with an accuracy-related measure called utility : the aggregated relevancy scores of the recommended items. Unfortunately, none of these three measures are differentiable, making it hard to optimize them, and restricting their immediate use to evaluation only. To remedy this, we reformulate them in the context of a probabilistic interpretation of recommender systems, resulting in differentiable versions. We show how these loss functions can be combined in a multi-objective optimization problem that we call FEIR (Fairness through Envy and Inferiority Reduction), used as a post-processing of the scores from any standard recommender system. Experiments on synthetic and real-world data show that the proposed approach effectively improves the trade-offs between inferiority, envy and utility, compared to the naive recommendation and the state-of-the-art method for the related problem of congestion alleviation in job recommendation. We discuss and enhance the practical impact of our findings on a wide range of real-world recommendation scenarios, and we offer implementations of visualization tools to render the envy and inferiority metrics more accessible.

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

Everyday artificial intelligence unveiled: Societal awareness of technological transformation

Research background: As Artificial Intelligence (AI) weaves into the fabric of daily life, its societal and economic implications underscore the urgency of embracing an environment conducive to its informed adoption. This requires a sophisticated understanding of the societal perception and adaptability to AI, emphasizing the importance of developing comprehensive AI literacy. Purpose of the article: This study inquiries into the sociodemographic underpinnings of AI literacy, aiming to demystify how knowledge about AI's capabilities in everyday tasks varies across individual population segments. It allows us to define the basic determinants that influence the differences in the individual population structures. It also reveals the potential risks associated with the use of AI. Methods: This study investigates the awareness of Artificial Intelligence (AI) in daily lives of the Czech population, focusing on the influence of socio-demographic factors. Utilizing computer-assisted web interviewing, we surveyed 1,041 respondents in April 2023, ensuring representativeness by applying quotas for age, gender, education, region, and residential area size. Our investigation spanned AI applications in sectors like customer service, music playlist recommendation, email sorting, healthcare, online shopping, and home devices. Findings & value added: Findings taken from descriptive statistics reveal variable AI awareness levels across different domains, with younger demographics exhibiting notably lower awareness in several areas. Regression analysis highlighted that awareness is significantly associated with gender, age, and education level. Regression analysis showed that males, younger age groups and those with higher levels of education were more likely to correctly answer majority of questions about the role of AI in everyday life. These insights are crucial for stakeholders aiming to enhance AI literacy, tailor communication strategies, and develop digital platforms, offering guidance for policymakers and market analysts in optimizing AI-related initiatives.

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.

Book Review: Computing Taste: Algorithms and the Makers of Music Recommendation

Book Review: Computing Taste: Algorithms and the Makers of Music Recommendation

Artificial intelligence robots based on machine learning and visual algorithms for interactive experience assistance in music classrooms

This article aims to study the application of artificial intelligence robots based on machine learning and visual algorithms in music classroom interactive experience assistance. In artificial intelligence robots, mobile adaptive networks can be used to optimize the perception and decision-making abilities of robots. By continuously learning and adapting to environmental changes, robots can better understand and respond to the interactive needs of music classrooms, providing more accurate and targeted auxiliary services. By learning and analyzing rich training data, robots can possess higher-level cognitive and comprehension abilities. In terms of music recommendation, the K-nearest neighbor algorithm is used to recommend music works that are suitable for students. By analyzing students’ music preferences and learning needs, robots provide personalized music recommendations to students based on this information, helping them better participate in and enjoy music classes. By applying machine learning and visual algorithms to music classroom interaction experiments, artificial intelligence robots based on machine learning and visual algorithms have the potential to assist in music classroom interaction experience, and teaching optimization strategies for music classrooms have been proposed.

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.