pyAudioAnalysis
librosa
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pyAudioAnalysis | librosa | |
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11 | 14 | |
5,668 | 6,681 | |
- | 2.2% | |
5.0 | 7.2 | |
29 days ago | 19 days ago | |
Python | Python | |
Apache License 2.0 | ISC License |
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pyAudioAnalysis
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How would I compare two voice recordings of the same sentence and advise one speaker how to get closer to the second?
I actually came up with an el cheapo version of what I want to accomplish that isn't perfect but without any research can implement it and it may actually prove useful to language learners. PM me if you're interested in hearing it and critiquing it. I can share here that I'm using this guy's multiple repos though: https://github.com/tyiannak/pyAudioAnalysis
- How do I run code only when an audio file has bass
- A Python library for audio feature extraction, classification, segmentation and applications
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Phonetic search for audio files
Update: From one researcher to another. I was referred to a Python Audio AI project . Once I determine exactly which module to use I should be smooth sailing. I'll send more updates soon.
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Clustering songs with different lengths
Hey folks, I'm looking into clustering audio files with features extracted by pyAudioAnalysis. However, every feature (I'm interested in MFCC, spectral centroid and spread, and BPM) is extracted for each frame of the song (by default 0.05s, excluding BPM that relates to the whole) so tracks with different lengths produce arrays with different shapes.
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AUDIO ANALYSIS WITH LIBROSA
To learn more about pyAudioAnalysis here you go.
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Creating Audio Features with PyAudio Analysis
Humans are great at classifying noises. We can hear a chirp and surmise that it belongs to a bird, we can hear an abstract noise and classify it as as speech with a particular meaning and definition. This relationship between humans and audio classification forms the basis of speech and human communication as a whole. Translating this incredible ability to computers on the other hand can be a difficult challenge to say the least. Whilst we can naturally decompose signals, how do we teach computers to do this, and how do we show what parts of the signal matter and what parts of the signal are irrelevant or noisy? This is where PyAudio Analysis comes in. PyAudio Analysis is an open source Python project by Theodoros Giannakopoulos, a Principle researcher of multimodal machine learning at the Multimedia Analysis Group of the Computational Intelligence Lab (MagCIL). The package aims to simplify the feature extraction and classification process by providing a number of helpful tools at can sift through the signal and create relevant features. These features can then be used to train models for classification tasks.
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[P] Feature extraction for acoustic signals
This might be relevant, which has a set of feature extraction methods implemented: https://github.com/tyiannak/pyAudioAnalysis/wiki/3.-Feature-Extraction
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Hacker News top posts: Dec 11, 2021
A library for audio feature extraction, regression, classification, segmentation\ (2 comments)
- Audio feature extraction, classification, segmentation and applications
librosa
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Open Source Libraries
librosa/librosa: Python library for audio and music analysis
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A Cross-Platform library for audio spectrogram and feature extraction, support mobile real-time computing
How does this compare to mature libraries for other platforms like librosa?
- Precious Advices About AI-supported Audio Classification Model
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What are the common audio feature tool libraries in python?
I use librosa now. What other useful audio feature extraction libraries are there?
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Looking for a program that will examine a folder full of mp3s or flacs and list out ones with lower or higher than average volume
librosa can do that easily but I think there is an easier way to find what are you looking for:
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Get amplitude of every audio frame of .wav
I have a .wav file, and using python, I'd like to get a list of every audio frame where the amplitude is at the resting position. How could I achieve this? I think the librosa library could do such a thing, but I'm struggling to find exactly how to do it. Any help would be greatly appreciated, thank you.
- Show HN: I'm building a browser-based DAW
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AUDIO ANALYSIS WITH LIBROSA
Librosa is a Python package developed for music and audio analysis. It is specific on capturing the audio information to be transformed into a data block. However, the documentation and example are good to understand how to work with audio data science projects.
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AUDIO CLASSIFICATION USING DEEP LEARNING
Hello! welcome once again to the continuation of the last blog post about audio analysis using the Librosa python library, if you missed this article don't worry here you can enjoy audio analysis techniques with Librosa.
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DATA AUGMENTATION IN NATURAL LANGUAGE PROCESSING
Changing pitch of the audio:- in this technique python package for audio analysis like Librosa is the best tool to go with, by adding effect on the audio pitch to create new audio data.
What are some alternatives?
pydub - Manipulate audio with a simple and easy high level interface
SpeechRecognition - Speech recognition module for Python, supporting several engines and APIs, online and offline.
essentia - C++ library for audio and music analysis, description and synthesis, including Python bindings
pyAcoustics - A collection of python scripts for extracting and analyzing acoustics from audio files.
kapre - kapre: Keras Audio Preprocessors
mingus - Mingus is a music package for Python
beets - music library manager and MusicBrainz tagger
Watson Developer Cloud Python SDK - :snake: Client library to use the IBM Watson services in Python and available in pip as watson-developer-cloud
audioread - cross-library (GStreamer + Core Audio + MAD + FFmpeg) audio decoding for Python
aeneas - aeneas is a Python/C library and a set of tools to automagically synchronize audio and text (aka forced alignment)
aubio - a library for audio and music analysis