pyAudioAnalysis | Python Audio Analysis Library : Feature Extraction | Machine Learning library
kandi X-RAY | pyAudioAnalysis Summary
kandi X-RAY | pyAudioAnalysis Summary
Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
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Top functions reviewed by kandi - BETA
- Compute the frequency spectrum for a given signal
- Compute the zero crossing of a frame
- Compute the harmonic function for a given frame
- Calculate the density of a signal
- This function creates a thumbnail of the audio
- Read audio file
- Reads an audio file
- Read an AIF file
- Train an HMM segmenter from a WAV file
- Extracts a beat extractor
- Train the classifier
- Argument parser
- Wrapper for silence removal
- Convert an annotation file to a folder
- Convert a WAV file into a spectrogram
- Wrapper function for chromagram
- Classify input file
- Performs multiple directory feature extraction
- Evaluate speaker diarization
- Extracts the feature extraction for each directory
- Classify a folder
- Convert a list of text to a list of colors
- Generate a histogram for a given model
- Function to segment classification
- Convert a csv file to a csv file
- Convert a list of strings to a set of colors
pyAudioAnalysis Key Features
pyAudioAnalysis Examples and Code Snippets
Lines of Code : 16
License : No License
Copyusage: dv_subs.py [-h] [--model_dir MODEL_DIR] [--temp_dir TEMP_DIR] [--silence_window SILENCE_WINDOW] [--silence_weight SILENCE_WEIGHT] input output
positional arguments:
input Input audio file name
output Output python3 deep_audio_features/combine/trainer.py -i 4class_small/music_small 4class_small/speech_small -c deep_audio_features/combine/config.yaml
from deep_audio_features.combine import trainer
trainer.train(["4class_small/music_small", "4class_small/ $ pip3 install -r requirements.txt
import soundfile as sf
y, sr = sf.read('existing_file.wav', dtype='int16')
msg = f"invalid level choice: {level} (choose from {parser.log_levels})"
pip3 install pyaudio
python -m pip install eyed3
pip install eyed3 --user
conda install eyed3
import soundfile as sf
import pyloudnorm as pyln
data, rate = sf.read("test.wav")
meter = pyln.Meter(rate) #
loudness = meter.integrated_loudness(data)
from pydub import AudioSegment
audio = AudioSegment.from_file('file.m4a')
(peaks, indexes) = octave.findpeaks(np.array(test), 'DoubleSided', nout=2)
Community Discussions
Trending Discussions on pyAudioAnalysis
QUESTION
I am trying to use pyAudioAnalysis to analyse an audio stream in real-time from a HTTP stream. My goal is to use the Zero Crossing Rate (ZCR) and other methods in this library to identify events in the stream.
pyAudioAnalysis only supports input from a file but converting a http stream to a .wav will create a large overhead and temporary file management I would like to avoid.
My method is as follows:
Using ffmpeg I was able to get the raw audio bytes into a subprocess pipe.
...ANSWER
Answered 2022-Mar-30 at 19:36You can try my ffmpegio package:
QUESTION
I have been looking at producing a multiplication function to be used in a method called Conflation. The method can be found in the following article (An Optimal Method for Consolidating Data from Different Experiments). The Conflation equation can be found below:
I know that 2 lists can be multiplied together using the following codes and functions:
...ANSWER
Answered 2021-Apr-02 at 17:12In the second prod_pdf you are using computed PDFs while in the first you were using defined distributions. So, in the second prod_pdf you already have the PDF. Thus, in the for loop you simply need to do p_pdf = p_pdf * pdf
From the paper you linked, we know that "For discrete input distributions, the analogous definition of conflation is the normalized product of the probability mass functions". So you need not only to take the product of PDFs but also to normalize it. Thus, rewriting the equation for a discrete distribution, we get
where F is the number of distributions we need to conflate and N is the length of the discrete variable x.
QUESTION
I am trying to make use of an open source Python program. I have a Python script configured exactly as per an example with all the pre-reqs installed, but I seem to be getting some kind of global Python error:
...ANSWER
Answered 2020-Dec-04 at 23:45The problem here is with the line suggested in the traceback:
QUESTION
I am doing a speech emotion recognition ML.
I currently use pyAudioAnalysis to do a multi-directory feature extraction. However, the dataset involved in audios containing a lot of approximately silent sections. My objective is to remove the approximately silent parts from all the audios then extract meaningful features.
My current approach is to use librosa to trim the silent parts.
ANSWER
Answered 2020-Oct-15 at 08:17- You need to
import librosa.displayseparately. See this issue for the reason. - You can use
librosa.output.write_wav(check the docs) to store the trimmed array as a wave file. E.g.librosa.output.write_wav(path, trimed_signal, Fs).
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install pyAudioAnalysis
Install dependencies: pip install -r ./requirements.txt
Install using pip: pip install -e .
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