<rss xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title>数字信号处理 - 标签 - HAKULA†CHANNEL</title><link>https://old.hakula.xyz/tags/%E6%95%B0%E5%AD%97%E4%BF%A1%E5%8F%B7%E5%A4%84%E7%90%86/</link><description>数字信号处理 - 标签 - HAKULA†CHANNEL</description><generator>Hugo -- gohugo.io</generator><language>zh-CN</language><managingEditor>i@hakula.xyz (Hakula)</managingEditor><webMaster>i@hakula.xyz (Hakula)</webMaster><lastBuildDate>Fri, 03 Jun 2022 15:14:00 +0800</lastBuildDate><atom:link href="https://old.hakula.xyz/tags/%E6%95%B0%E5%AD%97%E4%BF%A1%E5%8F%B7%E5%A4%84%E7%90%86/" rel="self" type="application/rss+xml"/><item><title>DSP - Project: 语音识别</title><link>https://old.hakula.xyz/posts/note/dsp/speech-recognition/</link><pubDate>Fri, 03 Jun 2022 15:14:00 +0800</pubDate><author>Hakula</author><guid>https://old.hakula.xyz/posts/note/dsp/speech-recognition/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://hakula-1257872502.file.myqcloud.com/images/3/article-covers/ca13be58-d114-46a1-a0e7-a7877a1474af_94819769.webp" referrerpolicy="no-referrer">
            </div><p>本项目实现了一个基础的语音识别系统，可以从 20 个给定单词中识别一段语音是其中的哪个单词。识别系统基于深度学习，以音频信号的 Mel 频率倒谱系数（MFCC）作为特征，通过一个卷积神经网络（CNN）进行训练。</p>
<p>Digital Signal Processing @ Fudan University, fall 2021.</p>]]></description></item><item><title>DSP - Lab 3: MFCC: Mel 频率的倒谱系数</title><link>https://old.hakula.xyz/posts/note/dsp/mfcc/</link><pubDate>Fri, 27 May 2022 04:29:00 +0800</pubDate><author>Hakula</author><guid>https://old.hakula.xyz/posts/note/dsp/mfcc/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://hakula-1257872502.file.myqcloud.com/images/3/article-covers/b8604b7b-53dd-4620-8cc4-34417baf5ae9_95680357.webp" referrerpolicy="no-referrer">
            </div><p>本实验中，我们实现了一个端点检测算法，并构造了一个 Mel 滤波器组处理信号的能量谱，最后利用离散余弦变换（DCT）得到了信号的 MFCC 系数。</p>
<p>Digital Signal Processing @ Fudan University, fall 2021.</p>]]></description></item><item><title>DSP - Lab 2: 语谱图</title><link>https://old.hakula.xyz/posts/note/dsp/spectrogram/</link><pubDate>Thu, 12 May 2022 08:50:00 +0800</pubDate><author>Hakula</author><guid>https://old.hakula.xyz/posts/note/dsp/spectrogram/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://hakula-1257872502.file.myqcloud.com/images/3/article-covers/83b43957-3908-4479-9350-68bcd4edeec4_102257565.webp" referrerpolicy="no-referrer">
            </div><p>本实验中，我们利用之前实现的 FFT 算法，生成了不同语音片段在不同窗口宽度下的语谱图。</p>
<p>Digital Signal Processing @ Fudan University, fall 2021.</p>]]></description></item><item><title>DSP - Lab 1: FFT: 快速傅立叶变换</title><link>https://old.hakula.xyz/posts/note/dsp/fft/</link><pubDate>Sat, 26 Mar 2022 01:37:00 +0800</pubDate><author>Hakula</author><guid>https://old.hakula.xyz/posts/note/dsp/fft/</guid><description><![CDATA[<div class="featured-image">
                <img src="https://hakula-1257872502.file.myqcloud.com/images/3/article-covers/d6012c5f-1973-467d-9fba-4ac6a11f4a53_102184125.webp" referrerpolicy="no-referrer">
            </div><p>本实验中，我们实现了一个基础的 FFT 算法，使用 Python 编写。</p>
<p>Digital Signal Processing @ Fudan University, fall 2021.</p>]]></description></item></channel></rss>