% Computation of ST-ZCR and STE of a Speech signal.
%
% Functions required: zerocross, sgn, winconv.
%
% Author: Nabin Sharma
% Date: 2009/03/15
[x,Fs] = wavread('so.wav'); % word is: so
x = x.';
N = length(x); % signal length
n = 0:N-1;
ts = n*(1/Fs); % time for signal
% define the window
wintype = 'rectwin';
winlen = 201;
winamp = [0.5,1]*(1/winlen);
This introduction takes a visionary look at ideal cognitive radios (CRs) that inte-
grate advanced software-defined radios (SDR) with CR techniques to arrive at
radios that learn to help their user using computer vision, high-performance
Speech understanding, global positioning system (GPS) navigation, sophisticated
adaptive networking, adaptive physical layer radio waveforms, and a wide range
of machine learning processes.
This paper presents a Hidden Markov Model (HMM)-based Speech
enhancement method, aiming at reducing non-stationary noise from Speech
signals. The system is based on the assumption that the Speech and the noise
are additive and uncorrelated. Cepstral features are used to extract statistical
information from both the Speech and the noise. A-priori statistical
information is collected from long training sequences into ergodic hidden
Markov models. Given the ergodic models for the Speech and the noise, a
compensated Speech-noise model is created by means of parallel model
combination, using a log-normal approximation. During the compensation, the
mean of every mixture in the Speech and noise model is stored. The stored
means are then used in the enhancement process to create the most likely
Speech and noise power spectral distributions using the forward algorithm
combined with mixture probability. The distributions are used to generate a
Wiener filter for every observation. The paper includes a performance
evaluation of the Speech enhancer for stationary as well as non-stationary
noise environment.
Although state of the art in many typical machine learning tasks, deep learning
algorithmsareverycostly interms ofenergyconsumption,duetotheirlargeamount
of required computations and huge model sizes. Because of this, deep learning
applications on battery-constrained wearables have only been possible through
wireless connections with a resourceful cloud. This setup has several drawbacks.
First, there are privacy concerns. Cloud computing requires users to share their raw
data—images, video, locations, Speech—with a remote system. Most users are not
willing to do this. Second, the cloud-setup requires users to be connected all the
time, which is unfeasible given current cellular coverage. Furthermore, real-time
applications require low latency connections, which cannot be guaranteed using
the current communication infrastructure. Finally, wireless connections are very
inefficient—requiringtoo much energyper transferredbit for real-time data transfer
on energy-constrained platforms.
INTRODUCTION In the past, adding Speech recording and playback capability to a product meant using a digital signal processor or a specialized audio chip. Now, using a simplified Adaptive Differential Pulse Code Modulation(ADPCM) algorithm, these audio capabilities can be added to any PICmicro device. This application note will cover the ADPCM compression and decompression algorithms, performance comparison of all PICmicro devices, and an application using a PIC16C72 micro-controller.DEFINITION OF TERMS step size -value of the step used for quantization of ana-log signals and inverse quantization of a number of steps.quantization -the digital form of an analog input signal is represented by a finite number of steps.adaptive quantization -the step size of a quantizer is dramatically changed with time in order to adapt to a changing input signal.inverse quantizer -a finite number of steps is converted into a digital representation of an analog signal.
VIP專區-嵌入式/單片機編程源碼精選合集系列（36）資源包含以下內容：1. C51
I2C
驅動程序
含頭文件.2. arm芯 ucos 下的開發框架.3. c8051f***的程序源代碼.4. 新華龍單片機的flash用法.5. 用C8051F300設計鋰離子電池充電器的解決方案.6. c8051f系列單片機 用過采樣和求均值提高ADC分辨率.7. C6000指令集
是對C6000指令集的文檔.8. VxWorks 編程手冊 VxWorks 編程手冊.9. proteus 6.7及其破解（絕對好用）.10. iic總線資料.11. 手機游戲禮包包
原理圖采用OrCad軟件或PowerPCB軟件打開
好玩的很
快來試試吧.12. 紅外測溫計
紅外線測溫 自動化設計
原理圖采用OrCad軟件或PowerPCB軟件打開
很完美.13. 9智能機器人禮包
智能機器人
單片機實現
功能齊全
運動流暢.14. 哈哈！終于找到了最新版本的UCGUI！
增加了不少的功能.15. Nios II是一個用戶可配置的通用RISC嵌入式處理器,這個文檔詳細介紹這個處理器的用法.16. 這是一個c語言寫的a/d轉換程序。利用i2c總線實現對24c02的存和取.17. 學ＤＳＰ時.18. 漢字液晶子程 液晶屏分為4行*12列漢字.19. 這是一個c語言描述的8位led顯示的源代碼.20. We demonstrate a method for encoding and decoding the [24,12,8] extended binary Golay code using a s.21. Training embedded apps to process Speech may be as easy as finding the right 8-bit micro. Don t let.22. The CC1000 RF transceiver is very easy
to interface with a microcontroller.
The chip is configured.23. One of the most important issues affecting
the implementation of microcontroller
software deals wi.24. AVR單片機開發中.25. 作基于日立公司superh系列微處理器嵌入式操作系統bootloader源代碼.26. touchsceen_test
├─ main.c C語言主源文件
├─ AscII6x8.c Ascii字符6x8點陣顯示格式數據
├─ lcd.c LCD顯示函數源文件
├─.27. iic_test
├─ main.c C語言主源文件
├─ iic.c IIC總線操作和24C040擦寫函數源文件
└─ iic.h IIC總線操作和24C040寫定義頭文件.28. Nexperia系統聲音實現的源碼.29. 89C516RD+的兩個AD轉換程序,89C51是22.1184MHZ,有64KB Flash 256B(DataRAM)+1024B(Ext.RAM)的性價比很高的MCU.30. 網上收集的VC多線程開發、嵌入式開發文檔.31. ucos-2在lpc2100上的移制例子。.32. mcf5307實驗源代碼.33. CPLD對DUSH的讀寫控制,LINUX下運行.解壓即可..34. HART協議由Rosemount公司開發且已向每個使用者開放HART協議采用標準的Bell 202頻移鍵控信號以1200波特通信以低電平加載于4mA~20mA模擬信號上.35. 中文MODBUS協議（完整版）涵蓋協議基本內容。不用我多說了吧！自己看好了。.36. μC_OS-II在Nios上的移植（共同學習ucosII）.37. SST28F040讀寫源程序,C語言編寫,方便移植.38. 基于ARM 的PDA拼音輸入法源程序,c語言編寫,方便移植.39. Cypress公司的USB芯片開發資料.40. at91 sam 系列arm7單片機程序下工具.
2.5 Neural Turing Machine
-
2.1 Model
-
.DS_Store
10KB
2.4 RNN Sequence-to-Sequence Model
-
2.8 One Shot Deep Learning
-
2.7 Deep Transfer Learning Lifelong Learning especially for RL
-
2.2 Optimization
-
1.4 Speech Recognition Evolution
-
1.2 Deep Belief Network(DBN)(Milestone of Deep Learning Eve)
-
1.3 ImageNet Evolution（Deep Learning broke out from here）
-
2.3 Unsupervised Learning Deep Generative Model
-
2.6 Deep Reinforcement Learning
