Music Classification Accuracy Research Articles

Enhanced Mood and Theme Recognition in Music Using Lyrical Sentiment Analysis

This paper presents an innovative approach to mood and theme recognition in music by leveraging lyrical sentiment analysis and audio signal processing techniques. As music plays a crucial role in emotional expression and cultural representation, understanding the mood and thematic elements within songs is essential for enhancing the listening experience. Traditional methods of music analysis often focus separately on audio signals or lyrics, failing to capture the intricate relationship between the two. To address this gap, we propose a hybrid model that integrates deep learning algorithms, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), with Natural Language Processing (NLP) techniques to provide a comprehensive understanding of musical content. In our study, we employ the CAL500 dataset, which contains a diverse selection of songs to facilitate effective analysis. The first component of our methodology involves lyrical sentiment analysis, where the preprocess the lyrics through techniques like tokenization, stemming, and stop-word removal to extract emotional context. This is complemented by audio signal processing, where features such as tempo, rhythm, and pitch are extracted using libraries like Librosa, allowing for a nuanced understanding of the music's tonal characteristics. The integration of insights derived from lyrical sentiment and audio signal processing enables the development of a robust classification system that accurately identifies mood and themes in music. Our results indicate that the combined approach significantly enhances mood recognition accuracy and reveals recurring themes within song lyrics, providing deeper insights into the underlying narrative and artistic intent. Ultimately, this research contributes to the fields of music theory, machine learning, and emotional analysis by proposing a novel framework for understanding the emotional landscape of music. The anticipated outcomes include improvements in music classification accuracy, insights into the emotional and cultural significance of songs, and potential applications in music therapy and personalized music experiences. By bridging characteristics, this study aims to transform how listeners interact with music, enhancing their emotional connection and appreciation of this art form

Music Classification and Detection of Location Factors of Feature Words in Complex Noise Environment

In order to solve the problem of the influence of feature word position in lyrics on music emotion classification, this paper designs a music classification and detection model in complex noise environment. Firstly, an intelligent detection algorithm for electronic music signals under complex noise scenes is proposed, which can solve the limitations existing in the current electronic music signal detection process. At the same time, denoising technology is introduced to eliminate the noise and extract the features from the signal. Secondly, from the perspective of audio and lyrics of song sentiment analysis and the unique characteristics of lyrics text, a lyric sentiment analysis method based on text title and position weight is proposed. Finally, considering the influence of the weight of feature words in different positions on the classification of lyrics, the analytic hierarchy process is used to calculate the weight of feature words in different positions of text title and lyrics before, in, and after the text. The results show that in the complex noise environment, the accuracy of music classification and detection of the proposed model is more than 90%, which is far beyond the control range of the actual application of music processing. The effect of music classification and detection is better than that of the contrast model, which has a certain practical application value.

A New Music Classification Method based on BP Neural Network

Abstract In order to improve the music classification accuracy, a new music classification method is proposed based on BP neural network in this paper. Firstly, the features are extracted, and then they are input into the BP neural network to train and obtain the optimal music classifier, lastly, the music classifier is tested by four music genres. The results show that the proposed method has improved the music classification accuracy and reduced the training time compared with traditional classification methods. And more importantly, the proposed method gives a new direction for the research and application of BP neural network.