A Hybrid Model for Music Genre Classification Using LSTM and SVM

Fulzele, Prasenjeet; Singh, Rajat Vikram; Kaushik, Naman; Pandey, Kavita

doi:10.1109/ic3.2018.8530557

Cited by 35 publications

(10 citation statements)

References 6 publications

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“…TT-150k [2] has been established for content-based micro-video background music recommendation which composes of approximately 3,000 different background music clips associated with 150,000 micro-videos from different uploaders. We extend it by categorizing each music with a music genre using a pre-trained music genre classification model [34] where we call the extended dataset as TT-150k-genre. The pre-trained classification model is based on music genre from the GTZAN music corpus [35] with multiple layers of LSTM Neural Nets, which is then utilized to extract the music genre of each music clip in the TT-150k.…”

Section: Methodsmentioning

confidence: 99%

Debiased Cross-modal Matching for Content-based Micro-video Background Music Recommendation

Yi¹,

Chen²

2022

Preprint

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Micro-video background music recommendation is a complicated task where the matching degree between videos and uploader-selected background music is a major issue. However, the selection of the user-generated content (UGC) is biased caused by knowledge limitations and historical preferences among music of each uploader. In this paper, we propose a Debiased Cross-Modal (DebCM) matching model to alleviate the influence of such selection bias. Specifically, we design a teacher-student network to utilize the matching of segments of music videos, which is professional-generated content (PGC) with specialized music-matching techniques, to better alleviate the bias caused by insufficient knowledge of users. The PGC data is captured by a teacher network to guide the matching of uploader-selected UGC data of the student network by KL-based knowledge transfer. In addition, uploaders' personal preferences of music genres are identified as confounders that spuriously correlate music embeddings and background music selections, resulting in the learned recommender system to over-recommend music from the majority groups. To resolve such confounders in the UGC data of the student network, backdoor adjustment is utilized to deconfound the spurious correlation between music embeddings and prediction scores. We further utilize Monte Carlo (MC) estimator with batch-level average as the approximations to avoid integrating the entire confounder space calculated by the adjustment. Extensive experiments on the TT-150k-genre dataset demonstrate the effectiveness of the proposed method towards the selection bias. The code is publicly available on: https://github.com/jing-1/DebCM.

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Section: Methodsmentioning

confidence: 99%

Debiased Cross-modal Matching for Content-based Micro-video Background Music Recommendation

Yi¹,

Chen²

2022

Preprint

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“…In order to make the network to have a better processing ability for long sequence data, researchers have put forward many improved methods. Fulzele et al (2018) fused LSTM and support vector machine (SVM) algorithms to classify music categories. The fusion model consists of two parts: firstly, correlation processing is carried out on the original audio and its features are extracted, and then these feature vectors are arranged and input into the two models, respectively.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Artistic Inheritance and Cultural Emotion Color Dissemination of Qin Opera

Han

2022

Front. Psychol.

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How to enable the computer to accurately analyze the emotional information and story background of characters in Qin opera is a problem that needs to be studied. To promote the artistic inheritance and cultural emotion color dissemination of Qin opera, an emotion analysis model of Qin opera based on attention residual network (ResNet) is presented. The neural network is improved and optimized from the perspective of the model, learning rate, network layers, and the network itself, and then multi-head attention is added to the ResNet to increase the recognition ability of the model. The convolutional neural network (CNN) is optimized from the internal depth, and the fitting ability and stability of the model are enhanced through the ResNet model. Combined with the attention mechanism, the expression of each weight information is strengthened. The multi-head attention mechanism is introduced in the model and a multi-head attention ResNet, namely, MHAtt_ResNet, is proposed. The network structure can effectively identify the features of the spectrogram, improve the weight information of spectrogram features, and deepen the relationship between distant information in long-time series. Through experiments, the proposed model has high emotional classification accuracy for Qin opera, and with the increase of the number of data sets, the model will train a better classification effect.

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“…The closest studies available are found to be relatively of mix findings, wherein one is found to be better than the other and vice-versa. For instance, SVM has been proven to be more accurate than LSTM in categorizing type of music [30]. On the other hand, another study found that LSTM performed better than SVM in categorizing CS tweets of an Arabic telco [31].…”

Section: Fig 3 -Lstm Memory Cell and Gatesmentioning

confidence: 99%

Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

Corpuz¹

2021

IJIE

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Analyzing natural language-based Customer Satisfaction (CS) is a tedious process. This issue is practically true if one is to manually categorize large datasets. Fortunately, the advent of supervised machine learning techniques has paved the way toward the design of efficient categorization systems used for CS. This paper presents the feasibility of designing a text categorization model using two popular and robust algorithms – the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Network, in order to automatically categorize complaints, suggestions, feedbacks, and commendations. The study found that, in terms of training accuracy, SVM has best rating of 98.63% while LSTM has best rating of 99.32%. Such results mean that both SVM and LSTM algorithms are at par with each other in terms of training accuracy, but SVM is significantly faster than LSTM by approximately 35.47s. The training performance results of both algorithms are attributed on the limitations of the dataset size, high-dimensionality of both English and Tagalog languages, and applicability of the feature engineering techniques used. Interestingly, based on the results of actual implementation, both algorithms are found to be 100% effective in accurately predicting the correct CS categories. Hence, the extent of preference between the two algorithms boils down on the available dataset and the skill in optimizing these algorithms through feature engineering techniques and in implementing them toward actual text categorization applications.

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A Hybrid Model for Music Genre Classification Using LSTM and SVM

Cited by 35 publications

References 6 publications

Debiased Cross-modal Matching for Content-based Micro-video Background Music Recommendation

Debiased Cross-modal Matching for Content-based Micro-video Background Music Recommendation

Deep Learning-Based Artistic Inheritance and Cultural Emotion Color Dissemination of Qin Opera

Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

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