This study explores the application of recurrent neural networks to recognize
emotions conveyed in music, aiming to enhance music recommendation systems and
support therapeutic interventions by tailoring music to fit listeners'
emotional states. We utilize Russell's Emotion Quadrant to categorize music
into four distinct emotional regions and develop models capable of accurately
predicting these categories. Our approach involves extracting a comprehensive
set of audio features using Librosa and applying various recurrent neural
network architectures, including standard RNNs, Bidirectional RNNs, and Long
Short-Term Memory (LSTM) networks. Initial experiments are conducted using a
dataset of 900 audio clips, labeled according to the emotional quadrants. We
compare the performance of our neural network models against a set of baseline
classifiers and analyze their effectiveness in capturing the temporal dynamics
inherent in musical expression. The results indicate that simpler RNN
architectures may perform comparably or even superiorly to more complex models,
particularly in smaller datasets. We've also applied the following experiments
on larger datasets: one is augmented based on our original dataset, and the
other is from other sources. This research not only enhances our understanding
of the emotional impact of music but also demonstrates the potential of neural
networks in creating more personalized and emotionally resonant music
recommendation and therapy systems.

本研究探讨了应用循环神经网络识别音乐中传达的情感，旨在通过将音乐调整到适合听众的情感状态，增强音乐推荐系统并支持治疗干预。我们使用 Russell 的情感象限将音乐分类为四个不同的情感区域，并开发了能够准确预测这些类别的模型。我们的方法涉及使用 Librosa 提取全面的音频特征，并应用各种循环神经网络架构，包括标准 RNN，双向 RNN 和 LSTM 网络。初步实验使用包含 900 个已标记情感象限的音频片段数据集进行，我们将神经网络模型的性能与一组基准分类器进行比较，并分析其在捕捉音乐表达中固有的时间动态方面的有效性。结果表明，在较小的数据集中，较简单的 RNN 架构的表现可能与更复杂的模型相当甚至更优。我们还在较大的数据集上应用了以下实验：一是基于我们原始数据集进行的增强，另一是来自其他来源。这项研究不仅增进了我们对音乐情感影响的理解，还展示了神经网络在创建更个性化和情感共鸣的音乐推荐和治疗系统方面的潜力。

使用循环神经网络进行音乐情感预测

Music Emotion Prediction Using Recurrent Neural Networks

In human contact, emotion is very crucial. Attributes like words, voice
intonation, facial expressions, and kinesics can all be used to portray one's
feelings. However, brain-computer interface (BCI) devices have not yet reached
the level required for emotion interpretation. With the rapid development of
machine learning algorithms, dry electrode techniques, and different real-world
applications of the brain-computer interface for normal individuals, emotion
categorization from EEG data has recently gotten a lot of attention.
Electroencephalogram (EEG) signals are a critical resource for these systems.
The primary benefit of employing EEG signals is that they reflect true emotion
and are easily resolved by computer systems. In this work, EEG signals
associated with good, neutral, and negative emotions were identified using
channel selection preprocessing. However, researchers had a limited grasp of
the specifics of the link between various emotional states until now. To
identify EEG signals, we used discrete wavelet transform and machine learning
techniques such as recurrent neural network (RNN) and k-nearest neighbor (kNN)
algorithm. Initially, the classifier methods were utilized for channel
selection. As a result, final feature vectors were created by integrating the
features of EEG segments from these channels. Using the RNN and kNN algorithms,
the final feature vectors with connected positive, neutral, and negative
emotions were categorized independently. The classification performance of both
techniques is computed and compared. Using RNN and kNN, the average overall
accuracies were 94.844 % and 93.438 %, respectively.

本研究通过机器学习方法，对脑机接口的 EEG 信号进行分析，利用离散小波变换，重复神经网络和 K 最近邻算法等技术，将与积极、中立和消极情绪相关的 EEG 信号进行分类，其分类性能高达 94.844％，相较于 KNN 算法的 93.438％更加优越。