Machine Learning systems have achieved outstanding performance in different
domains. In this paper machine learning methods have been applied to
classification task to classify music genre. The code shows how to extract
features from audio files and classify them using supervised learning into 2
genres namely classical and metal. Algorithms used are LogisticRegression, SVC
using different kernals (linear, sigmoid, rbf and poly), KNeighborsClassifier ,
RandomForestClassifier, DecisionTreeClassifier and GaussianNB.

本文应用机器学习方法，特别是监督学习，对音乐流派进行分类，通过提取音频文件特征并使用多种分类算法（如 LogisticRegression，SVC 和 DecisionTreeClassifier），将音乐分类为古典和重金属两个分类。

使用机器学习方法进行音频分类

Audio classification using ML methods

Lifelong audio feature extraction involves learning new sound classes
incrementally, which is essential for adapting to new data distributions over
time. However, optimizing the model only on new data can lead to catastrophic
forgetting of previously learned tasks, which undermines the model's ability to
perform well over the long term. This paper introduces a new approach to
continual audio representation learning called DeCoR. Unlike other methods that
store previous data, features, or models, DeCoR indirectly distills knowledge
from an earlier model to the latest by predicting quantization indices from a
delayed codebook. We demonstrate that DeCoR improves acoustic scene
classification accuracy and integrates well with continual self-supervised
representation learning. Our approach introduces minimal storage and
computation overhead, making it a lightweight and efficient solution for
continual learning.

本文介绍一种名为 DeCoR 的新的持续音频表征学习方法，通过预测延迟码书的量化索引，间接从早期模型蒸馏出知识到最新模型，从而提高了声学场景分类的准确性，并与持续自监督表示学习相融合。

DeCoR: 通过预测早期音频编码来避免知识遗忘

DeCoR: Defy Knowledge Forgetting by Predicting Earlier Audio Codes

This paper presents the method that underlies our submission to the untrimmed
video classification task of ActivityNet Challenge 2016. We follow the basic
pipeline of temporal segment networks and further raise the performance via a
number of other techniques. Specifically, we use the latest deep model
architecture, e.g., ResNet and Inception V3, and introduce new aggregation
schemes (top-k and attention-weighted pooling). Additionally, we incorporate
the audio as a complementary channel, extracting relevant information via a CNN
applied to the spectrograms. With these techniques, we derive an ensemble of
deep models, which, together, attains a high classification accuracy (mAP
$93.23\%$) on the testing set and secured the first place in the challenge.

本文介绍了我们提交至 ActivityNet Challenge 2016 非修剪视频分类任务的方法。我们使用基于时间段网络的基本流程，并通过其他技术提高模型性能。我们使用最新的深度模型体系结构，如 ResNet 和 Inception V3，并引入了新的聚合方案（top-k 和注意力加权池化）。此外，我们以音频作为补充通道，通过应用于频谱图的卷积神经网络提取相关信息。通过这些技术，我们导出了一个深度模型集合，它们共同在测试集上取得了高分类精度（mAP 93.23％），并获得了挑战赛的第一名。