Music Genre Classification Method Research Articles

A Music Genre Classification Method Based on Deep Learning

Digital music resources have exploded in popularity since the dawn of the digital music age. The music genre is an important classification to use when describing music. The function of music labels in discovering and separating digital music resources is crucial. In the face of a huge music database, relying on manual annotation to classify will consume a lot of cost and time, which cannot meet the needs of the times. The following are the paper’s primary research findings and innovations: to better describe the music, this article will be divided into multiple local musical instrument digital interface (MIDI) music passages, playing style close by analyzing passages, passages feature extracting, and feature sequence of passages. Extraction of note feature matrix, extraction of topic and segment division based on note feature matrix, research and extraction of effective features based on segment theme, and composition of feature sequence are all part of the process. Because of the shallow structure of standard classification methods, it is difficult for classifiers to learn temporal and semantic information about music. This research investigates recurrent neural networks (RNN) and attention using the distinctive sequence of input MIDI segments. To create data sets and conduct music categorization tests, collect 1920 MIDI files with genre labels from the Internet. The method for music classification is validated when it is combined with the experimental accuracy of equal length segment categorization.

Parallel Recurrent Convolutional Neural Networks-Based Music Genre Classification Method for Mobile Devices

With the rapid development of the mobile internet of things (IoTs) and mobile sensing devices, a large amount of mobile computing-oriented applications have attracted attention both from industry and academia. Deep learning based methods have achieved great success in artificial intelligence (AI) oriented applications. To advance the development of AI-based IoT systems, effective and efficient algorithms are in urgent need for IoT Edge Computing. Time-series data classification is an ongoing problem in applications for mobile devices (e.g. music genre classification on mobile phones). However, the traditional methods require field expertise to extract handcrafted features from the time-series data. Deep learning has been demonstrated to be effective and efficient in this kind of data. Nevertheless, the existing works neglect some of the sequential relationships found in the time-series data, which are significant for time-series data classification. Considering the aforementioned limitations, we propose a hybrid architecture, named the parallel recurrent convolutional neural network (PRCNN). The PRCNN is an end-to-end training network that combines feature extraction and time-series data classification in one stage. The parallel CNN and Bi-RNN blocks focus on extracting the spatial features and temporal frame orders, respectively, and the outputs of two blocks are fused into one powerful representation of the time-series data. Then, the syncretic vector is fed into the softmax function for classification. The parallel network structure guarantees that the extracted features are robust enough to represent the time-series data. Moreover, the experimental results demonstrate that our proposed architecture outperforms the previous approaches applied to the same datasets. We also take the music data as an example to conduct contrastive experiments to verify that our additional parallel Bi-RNN block can improve the performance of time-series classification compared with utilizing CNNs alone.

Music Genre Classification via Joint Sparse Low-Rank Representation of Audio Features

A novel framework for music genre classification, namely the joint sparse low-rank representation (JSLRR) is proposed in order to: 1) smooth the noise in the test samples, and 2) identify the subspaces that the test samples lie onto. An efficient algorithm is proposed for obtaining the JSLRR and a novel classifier is developed, which is referred to as the JSLRR-based classifier. Special cases of the JSLRR-based classifier are the joint sparse representation-based classifier and the low-rank representation-based one. The performance of the three aforementioned classifiers is compared against that of the sparse representation-based classifier, the nearest subspace classifier, the support vector machines, and the nearest neighbor classifier for music genre classification on six manually annotated benchmark datasets. The best classification results reported here are comparable with or slightly superior than those obtained by the state-of-the-art music genre classification methods.

Music Genre Classification Using Spectral Analysis and Sparse Representation of the Signals

In this paper, we proposed a robust music genre classification method based on a sparse FFT based feature extraction method which extracted with discriminating power of spectral analysis of non-stationary audio signals, and the capability of sparse representation based classifiers. Feature extraction method combines two sets of features namely short-term features (extracted from windowed signals) and long-term features (extracted from combination of extracted short-time features). Experimental results demonstrate that the proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. The extracted features are then fed into a sparse representation based classifier (SRC). Our experimental results on the GTZAN database demonstrate that the proposed method outperforms the other state of the art SRC approaches. Moreover, the computational efficiency of the proposed method is better than that of the other Compressive Sampling (CS)-based classifiers.