## 1 Introduction  
Automatic music genre classification is a well-established task in the field of machine learning [1]. Its objective is to accurately classify given music tracks into a specific genre or set of genres [2; 3]. The potential applications of music genre classification in music recommendation systems [4] and music streaming services [5] have led to extensive research in this area. Music genre classification typically involves two steps: acoustic feature extraction and classification. As a critical component of music genre classification, acoustic feature extraction extracts meaningful characteristics from music tracks. Traditional music features include loudness, rhythm [1], beat [1; 6], a zero-crossing rate [6], and Mel-frequency cepstral coefficients (MFCCs) [7; 8; 9]. In addition, researchers have also explored spectrograms based on Fourier transform [10; 11], wavelet transform [12], or constant-Q [13] transform, which contain rich time-frequency information (e.g., temporal information, periodic beat, rhythm, etc.) and can achieve a more satisfactory performance. The other critical component of music genre classification is designing classification algorithms to handle acoustic features. Classic machine learning algorithms include statistical methods, such as naive Bayes classifiers [14], random forests [15], and support vector machines (SVMs) [7; 8]. Meanwhile, some studies have revealed that classic machine learning models may not be suitable for large-scale data with diverse data distribution [4; 5]. With the advancement of deep learning [16] and computing resources, classification algorithms based on deep neural networks continue to grow in popularity. Most researchers choose recurrent neural networks (RNNs) [17], convolutional neural networks (CNNs) [18; 19], or Transformers [9] as the classification backbone for music genre classification. Such deep learning-based methods could capture the latent information in acoustic features (e.g., timbre information and semantic information ), thus achieving a better performance in real-world applications. However, there are still some limitations to the existing methods. Based on the analysis of the music signals, we observe that the genre of music is a very broad concept [20]. Music tracks belonging to the same genre may have diverse acoustic characteristics, such as the rhythm and beats. Figure 1 illustrates the significant variation in spectrograms of music tracks belonging to the \"blues\" genre. The drawback of the existing methods is that they are not good at dealing with such diverse data distributions with large intra-class differences. To correctly classify Blues A and Blues C into the same genre, the model needs to further capture deep latent information. However, when the amount of data is insufficient for \"deeper digging,\" it can lead to false inductive bias and adversely affect the classification accuracy. In this work, we specifically address the difficult problem of music genre classification with large differences in intra-class data distribution. We propose the locally activated gated neural network (LGNet) for music genre classification. LGNet includes multiple multi-layer perceptrons (MLPs) [21] and a gated routing layer [22] on the backbone of the classification network. We design several MLPs and employ them as multi-learners, focusing on knowledge in different aspects. The gated routing layer is applied to determine the allocation of inputs by calculating the matching degree between the input representations and each MLP. When the model encounters a certain sample, only the MLP with a high matching degree is activated, while the remaining MLPs are deactivated. In this manner, the model can adaptively allocate the most appropriate network layer according to the input samples.