The aim of this study is to teach an algorithm how to recognize different
types of music. Users will submit songs for analysis. Since the algorithm
hasn't heard these songs before, it needs to figure out what makes each song
unique. It does this by breaking down the songs into different parts and
studying things like rhythm, melody, and tone via supervised learning because
the program learns from examples that are already labelled. One important thing
to consider when classifying music is its genre, which can be quite complex. To
ensure accuracy, we use five different algorithms, each working independently,
to analyze the songs. This helps us get a more complete understanding of each
song's characteristics. Therefore, our goal is to correctly identify the genre
of each submitted song. Once the analysis is done, the results are presented
using a graphing tool, making it easy for users to understand and provide
feedback.

用五个不同独立的算法对用户上传的歌曲进行分析，通过有监督学习从事例中学习来识别歌曲的独特之处，特别考虑音乐的流派，最终用图形工具展示分析结果。

通过多算法分析和用户友好可视化提升音乐流派分类

Enhancing Music Genre Classification through Multi-Algorithm Analysis  and User-Friendly Visualization

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

Existing music recognition applications require a connection to a server that
performs the actual recognition. In this paper we present a low-power music
recognizer that runs entirely on a mobile device and automatically recognizes
music without user interaction. To reduce battery consumption, a small music
detector runs continuously on the mobile device's DSP chip and wakes up the
main application processor only when it is confident that music is present.
Once woken, the recognizer on the application processor is provided with a few
seconds of audio which is fingerprinted and compared to the stored fingerprints
in the on-device fingerprint database of tens of thousands of songs. Our
presented system, Now Playing, has a daily battery usage of less than 1% on
average, respects user privacy by running entirely on-device and can passively
recognize a wide range of music.

本文提出了一种低功耗音乐识别器，能在移动设备上完全运行，无需用户交互，并且通过在 DSP 芯片上运行一个小型音乐检测器来节省电量，仅在其确信存在音乐时唤醒主应用处理器，通过音频指纹技术比对本地数万首歌曲的指纹数据库，能有效地被动识别广泛的音乐，此系统平均每天仅使用不到 1% 的电量并且完全在设备上运行，尊重用户的隐私。