signal-processing

I have a couple audio files that I open in Pydub with AudioSegment . I want to decrease the audio quality from frame rate 22050 to 16000 Hz. (One channel files) If I simply change the frame rate of AudioSegment, what I get is the exact same wave played in slower speed. Well, fair enough. But how do I actually change the waves to fit a lower quality, same speed playback? (Manual interpolation is the only thing I can think of, but I don't want to get into that trouble) You can use: sound = AudioSegment.from_file(…) sound = sound.set_frame_rate(16000) 来源： https://stackoverflow.com/questions

I'm trying to write an Android app that analyzes content from the user's music library. Let's assume that these are mp3 files on the SD drive, for starters. I'm able to find Java algorithms to analyze music files, but I can't find an API to read and decode the files (not play them. There's an API to play the files, and even classes for audio effects, but I don't see any way for an app to get to the decoded data from a music file. I can read from microphone. J2SE has a class AudoInputStream,but it's not part of Android. Any suggestions? My real purpose was to be able to look at the files in my

For what kind of applications can i use dsp core of beagleboard? Can i use the DSP acceleration for background subtraction algorithm in OpenCV? You can use the DSP for all kinds of computations. It is a general purpose CPU optimized for DSP applications. So yes, even floating point stuff will work albeit the performance will not be great. The DSP really shines if you do integer computations over large arrays of data. Here the DSP can easily compute so fast that the time to transfer data from and to memory becomes the bottleneck. To give you a figure what is possible: I have an algorithm

I'm new with Python and I'm completely stuck when filtering a signal. This is the code: import numpy as np import matplotlib.pyplot as plt from scipy import signal fs=105e6 fin=70.1e6 N=np.arange(0,21e3,1) # Create a input sin signal of 70.1 MHz sampled at 105 MHz x_in=np.sin(2*np.pi*(fin/fs)*N) # Define the "b" and "a" polynomials to create a CIC filter (R=8,M=2,N=6) b=np.zeros(97) b[[0,16,32,48,64,80,96]]=[1,-6,15,-20,15,-6,1] a=np.zeros(7) a[[0,1,2,3,4,5,6]]=[1,-6,15,-20,15,-6,1] w,h=signal.freqz(b,a) plt.plot(w/max(w),20*np.log10(abs(h)/np.nanmax(h))) plt.title('CIC Filter Response')

The following code generates a spectrogram using either scipy.signal.spectrogram or matplotlib.pyplot.specgram . The color contrast of the specgram function is, however, rather low. Is there a way to increase it? import numpy as np from scipy import signal import matplotlib.pyplot as plt # Generate data fs = 10e3 N = 5e4 amp = 4 * np.sqrt(2) noise_power = 0.01 * fs / 2 time = np.arange(N) / float(fs) mod = 800*np.cos(2*np.pi*0.2*time) carrier = amp * np.sin(2*np.pi*time + mod) noise = np.random.normal(scale=np.sqrt(noise_power), size=time.shape) noise *= np.exp(-time/5) x = carrier + noise

I'm getting wrong frequency, I don't understand why i'm getting wrong values.since i have calculating as per instructions followed by stackoverflow. I've used FFT from http://introcs.cs.princeton.edu/java/97data/FFT.java.html and complex from http://introcs.cs.princeton.edu/java/97data/Complex.java.html audioRec.startRecording(); audioRec.read(bufferByte, 0,bufferSize); for(int i=0;i<bufferSize;i++){ bufferDouble[i]=(double)bufferByte[i]; } Complex[] fftArray = new Complex[bufferSize]; for(int i=0;i<bufferSize;i++){ fftArray[i]=new Complex(bufferDouble[i],0); } FFT.fft(fftArray); double[]