Venue: NAACL 2019

# Review-Driven Multi-Label Music Style Classification

by Exploiting Style Correlations

Guangxiang Zhao1, Jingjing Xu2, Qi Zeng2, Xuancheng Ren2, Xu Sun1,2

###### Abstract

This paper explores a new natural language processing task, review-driven multi-label music style classification. This task requires systems to identify multiple styles of music based on its reviews on websites. The biggest challenge lies in the complicated relations of music styles. To tackle this problem, we propose a novel deep learning approach to automatically learn and exploit style correlations. Experiment results show that our approach achieves large improvements over baselines on the proposed dataset. Furthermore, the visualized analysis shows that our approach performs well in capturing style correlations.1

Footnote 1: The code and the dataset are available at [https://github.com/lancopku/RMSC](https://github.com/lancopku/RMSC)

+
Footnote †: _Proceedings of NAACL-HLT 2019_, pages 2884–2891

Minneapolis, Minnesota, June 2 - June 7, 2019. ©2019 Association for Computational Linguistics

## 1 Introduction

As music style (e.g., Jazz, Pop, and Rock) is one of the most frequently used labels for music, music style classification is an important task for applications of music recommendation, music information retrieval, etc. Several methods have been proposed for automatic music style classification Qin and Ma (2005); Zhou et al. (2006); Wang et al. (2009); Choi et al. (2017). Most of them mainly focus on using audio information to identify styles. Motivated by the fact that a pieces of music could has different styles, several studies Wang et al. (2009); Oramas et al. (2017) also aim at multi-label music style classification.

Although these methods make promising progress, they are limited in two aspects. First, not all audio data is available in real-world applications because of copyright restrictions, which limits the generalization ability. Second, some of them are based on a strong assumption that a piece of music should be assigned with only one style. Different from these studies, we focus on using easily obtained reviews in conjunction with multi-label music style classification. The motivation comes from the fact that lots of user reviews contain rich style-related information, which can be used for music style classification.

The major challenge of this task lies in the complicated correlations of music styles. For example, Soul Music2 contains elements of R&B and Jazz. These three labels can be used alone or in combination. Traditional multi-label classification methods may mistake the true label Soul Music, R&B, Jazz for the false label R&B, Jazz. If well learned, style relations are useful knowledge for improving the performance, e.g., increasing the probability of Soil Music if we find that it is heavily linked with two high probability labels: R&B and Jazz. Therefore, to better exploit style correlations, we propose a novel deep learning approach with two parts: a label-graph based neural network, and a soft training mechanism with correlation based continuous label representation.

Our contributions are listed as follows:

* To the best of our knowledge, this work is the first to explore review-driven multi-label music style classification.
* To learn the relations among music styles, we propose a label-graph based neural network and a soft training mechanism with correlation-based label representation.

## 2 Related work

This paper is related with music style classification and multi-label classification. In this section, we give a detailed introduction about the related studies.





### Music Style Classification

Previous work mainly focuses on using audio information to identify music styles. Traditional machine learning algorithms are adopted in these studies, such as Support Vector Machine (SVM) Xu et al. (2003), Hidden Markov Model (HMM) Chai and Vercoe (2001); Pikrakis et al. (2006), and Decision Tree (DT) Zhou et al. (2006). In addition to audio information, Fell and Sporleder (2014) also propose to classify music by statistical analysis of lyrics. Motivated by the fact that a piece of music could has different styles, several studies Wang et al. (2009); Oramas et al. (2017) also aim at multi-label music style classification. Different from these studies, we focus on using easily obtained reviews in conjunction with multi-label music style classification.

### Multi-Label Classification

Multi-label classification has been widely applied to diverse problems, including image classification Qi et al. (2007); Wang et al. (2008), audio classification Boutell et al. (2004); Sanden and Zhang (2011), web mining Kazawa et al. (2004), information retrieval Zhu et al. (2005); Gopal and Yang (2010), etc. Compared with the existing multi-label learning methods Wei et al. (2018); Li et al. (2018b, a); Yang et al. (2018); Lin et al. (2018), our method has the following novelties: a label graph that explicitly models the relations of styles; a soft training mechanism that introduces correlation-based continuous label representation.

## 3 Review-Driven Multi-Label Music Style Classification

### Task Definition

Given several reviews from a piece of music, this task requires models to predict a set of music styles. Assume that \(X=\{\mathbf{x}_{1},\ldots,\mathbf{x}_{i},\ldots,\mathbf{x}_{K}\}\) denotes the input \(K\) reviews, and \(\mathbf{x}_{i}=x_{i,1},\ldots,x_{i,J}\) represents the \(i^{th}\) review with \(J\) words. The term \(Y=\{y_{1},y_{2},\ldots,y_{M}\}\) denotes the gold set with \(M\) labels, and \(M\) varies in different samples. The target of review-driven multi-label music style classification is to learn the mapping from input reviews to style labels.

### Dataset

The dataset is collected from a popular Chinese music review website,3 where registered users are allowed to comment on all released music albums. Each sample includes a music title, a set of human annotated styles, and associated reviews. An example is shown in Table 1.

Footnote 3: [https://music.douban.com](https://music.douban.com)

In order to build a high-quality dataset, we refer to the literature about music styles. We merge similar music styles and delete music styles that violate the music classification list. 22 styles are defined in our dataset.4 For user reviews, we first delete reviews with too little information by rule-based methods and then select top 40 voted reviews. Music samples with too few reviews are also deleted. The constructed datataset contains over 7.1k samples, 288K reviews, and 3.6M words.

Footnote 4: Alternative Music, Britpop, Classical Music, Country Music, Dark Wave, Electronic Music, Folk Music, Heavy Metal Music, Hip-Hop, Independent Music, Jazz, J-Pop, New-Age Music, GST, Piano Music, Pop, Post-Punk, Post-Rock, Funk, R&B, Rock, and Soul Music.

## 4 Proposed Approach

The proposed approach contains two parts: a label-graph based neural network and a soft training mechanism with continuous label representation. An illustration of the proposed method is shown in Figure 1.

### Label-Graph Based Neural Network

The first layer is a hierarchical attention layer Yang et al. (2016) that lets the model to pay more or less attention to individual words

\begin{table}
\begin{tabular}{|l|p{284.5pt}|} \hline Music Title & Mozart: The Great Piano Concertros, Vol.1 \\ \hline Styles & Classical Music, Piano Music \\ \hline  & (1) I’ve been listening to **classical** music all the time. \\  & (2) Mozar is always good. There is a reason he is ranked in the top 3 of lists of greatest **classical** composers. \\  & (3) The sound of **pliano** brings me peace and relaxation. \\  & (4) This volume of Mozart concerts is superb. \\ \hline \end{tabular}
\end{table}
Table 1: An illustration of review-driven multi-label music style classification. For easy interpretation, we select a simple and clear example where styles can be easily inferred from reviews. In practice, the correlation between styles and associated reviews is relatively complicated.



and reviews when constructing "raw" label probability distribution \({\mathbf{z}}\).

Label Graph.To explicitly take advantage of the label correlations when classifying music styles, we add a label graph layer to the network. This layer takes \({\mathbf{z}}\) as input and generates a "soft" label probability distribution \({\mathbf{e}}\).

Formally, we denote \({\cal G}\in R_{m\times m}\) as the label graph, where \(m\) is the number of labels, \({\cal G}\) is initialized by an identity matrix. An element \({\cal G}[l_{i},l_{j}]\) is a real-value score indicating how likely label \(l_{i}\) and label \(l_{j}\) are related. The graph \({\cal G}\) is a part of parameters and can be learned by back-propagation.

Then, given the "raw" label probability distribution \({\mathbf{z}}\) and the label graph \({\cal G}\), the output of this layer is:

\[{\mathbf{e}}={\rm sigmoid}({\mathbf{z}}\cdot{\cal G}). \tag{1}\]

The probability of \(l_{j}\) is not only determined by the current classification result, but also determined by other labels probabilities and their correlations to \(l_{j}\). For example, the probability of a label heavily linked with many high-probability labels will be increased.

### Soft Training

Given a predicted label probability distribution \({\mathbf{e}}\) and a target discrete label representation \({\mathbf{y}}\), the typical loss function is computed as

\[L(\theta)={\cal H}({\mathbf{y}},{\mathbf{e}})=-\sum_{i=1}^{m}y_{i}\log e_{i}, \tag{2}\]

where \(\theta\) denotes all parameters, and \(m\) is the number of the labels. The function \({\cal H}\) denotes the cross entropy between two distributions.

However, the widely used discrete label representation does not apply to the task of music style classification, because the music styles are not mutually exclusive and highly related to each other. The discrete distribution without label relations makes the model over-distinguish the related labels. Therefore, it is hard for the model to learn the label correlations.

Instead, we propose a soft training method by combining a discrete label representation \({\mathbf{y}}\) with a correlated-based continuous label representation \({\mathbf{y}}^{\prime}\). The probability gap between two similar labels in \({\mathbf{y}}^{\prime}\) should not be large.

A straight-forward approach to produce the continuous label representation is to use the label graph matrix \({\cal G}\) to transform the discrete representation \({\mathbf{y}}\) into a continuous form:

\[{\mathbf{y}}^{\prime}={\mathbf{y}}\cdot{\cal G}. \tag{3}\]

Based on the discrete label representation \({\mathbf{y}}\) and continuous label representation \({\mathbf{y}}^{\prime}\), we define the loss function as

\[Loss(\theta)={\cal H}({\mathbf{e}},{\mathbf{y}})+{\cal H}({\mathbf{e}},{\mathbf{ y}}^{\prime}), \tag{4}\]

where the loss \({\cal H}({\mathbf{e}},{\mathbf{y}})\) aims to correctly classify labels, and the loss \({\cal H}({\mathbf{e}},{\mathbf{y}}^{\prime})\) aims to avoid the over-distinguishing problem and to better learn label correlations.

## 5 Experiment

In this section, we evaluate our approach on the proposed dataset. We first introduce evaluation metrics, then show experiment results and give a detailed analysis. The training details can be found at Appendices.

Figure 1: An illustration of the proposed approach. Left: The label-graph based neural network. Right: The soft training method. The label graph defines the relations of labels. \({\mathbf{e}}\) is the label probability distribution. Soft training means that we combine the continuous label representation \({\mathbf{y}}^{\prime}\) and the discrete label representation \({\mathbf{y}}\) together to train the model. The hierarchical attention layer is responsible for extracting style-related information. The label graph layer and soft training are used for exploiting label correlations.

 



### Evaluation Metrics

Multi-label classification requires different evaluation metrics compared with traditional single-label classification. In this paper, we use the following widely-used evaluation metrics for multi-label classification.

* F1-score: We calculate macro F1 and micro F1, respectively. Macro F1 computes the metric independently for each label and then takes the average as the final score. Micro F1 aggregates the contributions of all labels to compute the average score.
* One-Error: One-error evaluates the fraction of examples whose top-ranked label is not in the gold label set.
* Hamming Loss: Hamming loss counts the fraction of the wrong labels to the total number of labels.

### Baselines

We implement several widely-used multi-label classification methods as baselines, such as ML-KNN [20], Binary Relevance [37], Classifier Chains [54], Label Powerset [36]. The details of baselines can be found at Appendices.

### Results

The results on the test set are summarized in Table 2. The proposed approach significantly outperforms the baselines, with micro F1 of 64.5, macro F1 of 54.4, and one-error of 22.6, improving the metrics by 10.6, 21.4, and 7.9 respectively. The improvements are attributed to two parts, a hierarchical attention network and a label correlation mechanism. Only using the hierarchical attention network outperforms the baselines, which shows the effectiveness of hierarchically paying attention to different words and sentences. The greater F1-score is achieved by adding the proposed label graph, which demonstrates that the proposed label graph helps a lot by taking advantage of label correlations.

It can be clearly seen that with the help of soft training, the proposed method achieves the best performance. Especially, the micro F-score is improved from 62.8 to 64.5, and the one-error is reduced from 23.4 to 22.6. With the new loss function, the model not only knows how to distinguish the right labels from the wrong ones, but also can learn the label correlations that are useful knowledge, especially when the input data contains too much style unrelated words for the model to extract all necessary information.

### Visualization Analysis

Figure 2 shows a whole heatmap of the automatically learned label graph, for convenience of display, we have subtracted the label graph by an identity matrix. We can see from the picture that some obvious music style relations are well captured. For "Country Music", the most related label is "Folk Music". In reality, these two music styles are highly similar and the boundary between them is not well-defined. For three kinds of rock music, "Heavy Metal Music", "Britpop Music", and "Alternative Musi

\begin{table}
\begin{tabular}{l|c|c|c|c} \hline
**Models** & **OE(-)** & **HL(-)** & **Macro F1(+)** & **Micro F1(+)** \\ \hline ML-KNN & 77.3 & 0.094 & 23.6 & 38.1 \\ Binary Relevance & 74.4 & 0.083 & 24.7 & 41.8 \\ Classifier Chains & 67.5 & 0.107 & 29.9 & 44.3 \\ Label Powerset & 56.2 & 0.096 & 37.7 & 50.3 \\ MLP & 71.5 & 0.081 & 29.8 & 45.8 \\ CNN & 37.9 & 0.099 & 32.5 & 49.3 \\ LSTM & 30.5 & 0.089 & 33.0 & 53.9 \\ \hline
**HAN** & 25.9 & 0.079 & 52.1 & 61.0 \\
**+Label Graph** & 23.4 & 0.077 & 54.2 & 62.8 \\
**+ Soft Training** & **22.6** & **0.074** & **54.4** & **64.5** \\ \hline \end{tabular}
\end{table}
Table 2: Comparisons between our approach and the baselines on the test set. OE and HL denote one-error and hamming loss respectively. HAN denotes the hierarchical attention network. “(+)” represents that higher scores are better and “(-)” represents that lower scores are better. It can be seen that the proposed approach significantly outperforms the baselines.

Figure 2: A heatmap of the automatically learned label graph. Deeper color represents closer relation. We can see that some obvious relations are well captured by the model, e.g., “Heavy Metal Music (Metal)” and “Rock”, “Country Music (Country)” and “Folk”.



rectly captures that the most related label for them is "Rock". For a more complicated relation where "Soul Music" is highly linked with two different labels, "R&B" and "Jazz", the label graph also correctly capture such relation. These examples demonstrate that the proposed approach performs well in capturing relations among music styles.

### Selected Exmaples

For clearer understanding, we compare several examples generated with and without the label correlation mechanism in Table 3. By comparing gold labels and predicted labels generated by different methods, we find that the proposed label correlation mechanism identifies the related styles more precisely. This is mainly attributed to the learned label correlations. For example, the correct prediction in the first example shows that the label correlation mechanism captures the close relation between "Britpop" and "Rock", which helps the model to generate an appropriate prediction.

### Error Analysis

Although the proposed method has achieved significant improvements, we also notice that there are some failure cases. In this section, we give the detailed error analysis.

First, the proposed method performs worse on the styles with low frequency in the training set. Table 4 compares the performance on the top 5 music styles of highest and lowest frequencies. As we can see, the top 5 fewest music styles get much worse results than top 5 most music styles. This is because the label distribution is highly imbalanced where unpopular music styles have too little training data.

Second, we find that some music items are wrongly classified into the styles that are similar with the gold styles. For example, a sample with a gold set [Country Music] is wrongly classified into [Folk] by the model. The reason is that some music styles share many common elements and only subtly differ from each other. It poses a great challenge for the model to distinguish them. For future work, we would like to research how to effectively address this problem.

## 6 Conclusions

In this paper, we focus on classifying multi-label music styles with user reviews. To meet the challenge of label correlations, we propose a label-graph neural network and a soft training mechanism. Experiment results have showed the effectiveness of the proposed approach. The visualization of label graph also shows that our method performs well in capturing label correlations.

#### Acknowledgments

We thank all reviewers for providing the thoughtful and constructive suggestions. This work was supported in part by National Natural Science Foundation of China (No. 61673028). Email correspondence to Xu Sun.

\begin{table}
\begin{tabular}{l c c} \hline
**Most Styles** & **\% of Samples** & **Micro F1** \\ \hline Rock & 30.4 & 75.8 \\ Independent Music & 30.0 & 64.8 \\ Pop & 26.2 & 67.1 \\ Folk Music & 21.9 & 73.7 \\ Electronic Music & 13.9 & 61.8 \\ \hline
**Fewest Styles** & **\% of Samples** & **Micro F1** \\ \hline Jazz & 4.3 & 37.5 \\ Heavy Metal Music & 3.9 & 55.6 \\ Hip-Hop & 3.1 & 7.5 \\ Post-punk & 2.5 & 17.1 \\ Dark Wave & 1.3 & 17.4 \\ \hline \end{tabular}
\end{table}
Table 4: The performance of the proposed method on the most and fewest styles.

\begin{table}
\begin{tabular}{|l|l|c|} \hline
**Ground Truth** & **Without LCM** & **With LCM** \\ \hline Britpop®, Rock & Britpop & Britpop, Rock \\ \hline Hip-Hop®, Pop & Electronic Music, Pop & Pop, R\&B \\ \cline{2-3} Top & Pog, R\&B \\ \cline{2-3} Country Music; Polk, Pop & Country Music, Pop & Popular Music, Pop, Folk \\ \hline Classical Music, New-Age Music*, Piano Music & Piano Music, Social Music & Number-Age Music, Classical Music \\ \hline \end{tabular}
\end{table}
Table 3: Examples generated by the methods with and without the label correlation mechanism (LCM). The labels correctly predicted by two methods are shown in blue. The labels correctly predicted by the method with the label correlation mechanism are shown in orange. We can see that the method with the label correlation mechanism classifies music styles more precisely.

 



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Appendices

### Training Details

In the proposed method and baselines, we use skip gram Mikolov et al. (2013) to get pre-trained word embeddings of reviews. The Jieba toolkit is used to split sentences into words. To help with the training of the label graph, we use soft target mechanism on the continuous label representations Hinton et al. (2015); Sun et al. (2017); Liu et al. (2017) and add the negative of the l2 loss of the difference between the label graph and the identity matrix to the loss function. For evaluation, we introduce a hyper-parameter \(p\). If the probability of a style is greater than \(p\), we consider it as one of the final music styles. We tune hyperparameters based on the performance on the validation set. We set \(p\) to 0.2, hidden size to 128, embedding size to 128, vocabulary size to 135K, learning rate to 0.001, and batch size to 128. The optimizer is Adam Kingma and Ba (2014) and the maximum training epoch is set to 100. We choose parameters with the best Micro F1 scores on the validation set and then use the selected parameters on the test set.

### Baselines

We implement the following widely-used multi-label classification methods for comparison. Their inputs are the music representations which are produced by averaging review representations. The review representation is obtained by averaging word embeddings.

* ML-KNN Zhang and Zhou (2007): It is a multi-label learning approach derived from the traditional K-Nearest Neighbor (KNN) algorithm.
* Binary Relevance Tsoumakas et al. (2010): It decomposes a multi-label learning task into a number of independent binary learning tasks. It learns several single binary models without considering the dependencies among labels.
* Classifier Chains Read et al. (2011): It takes label dependencies into account and keeps the computational efficiency of the binary relevance method.
* Label Powerset Tsoumakas and Vlahavas (2007): All classes assigned to an example are combined into a new and unique class in this method.
* MLP: It feeds the music representations into a multi-layer perceptron, and generates the probability of music styles through a sigmoid layer.

Different from the above baselines, the following two methods only take word embeddings as inputs. Similar to MLP, they produce label probability distribution by a sigmoid function.

* CNN: It includes two layers of CNN which has multiple convolution kernels.
* LSTM: It includes two layers of LSTM, which processes words and sentences separately to get the music representations.