Pros of librosa

• More comprehensive and feature-rich library for music and audio analysis
• Better documentation and community support
• Actively maintained with frequent updates

Cons of librosa

• Steeper learning curve for beginners
• May be overkill for simple audio processing tasks
• Slower performance for some operations compared to pyAudioAnalysis

Code Comparison

pyAudioAnalysis:

import audioBasicIO
import audioFeatureExtraction

[Fs, x] = audioBasicIO.read_audio_file("sample.wav")
F, f_names = audioFeatureExtraction.stFeatureExtraction(x, Fs, 0.050*Fs, 0.025*Fs)

librosa:

import librosa

y, sr = librosa.load("sample.wav")
mfccs = librosa.feature.mfcc(y=y, sr=sr)
chroma = librosa.feature.chroma_stft(y=y, sr=sr)

Both libraries offer audio file reading and feature extraction capabilities. pyAudioAnalysis provides a more straightforward approach for basic audio analysis, while librosa offers more advanced and customizable features for music and audio processing.

Pros of aubio

• Written in C, offering better performance for low-level audio processing tasks
• Provides a wider range of audio analysis algorithms, including pitch detection, onset detection, and beat tracking
• Offers bindings for multiple programming languages, including Python, making it versatile for different development environments

Cons of aubio

• Steeper learning curve due to its lower-level implementation and more complex API
• Less focus on machine learning-based audio analysis compared to pyAudioAnalysis
• Requires compilation and may have more complex setup process, especially on certain platforms

Code Comparison

pyAudioAnalysis example:

from pyAudioAnalysis import audioBasicIO
from pyAudioAnalysis import audioFeatureExtraction

[Fs, x] = audioBasicIO.read_audio_file("example.wav")
F, f_names = audioFeatureExtraction.stFeatureExtraction(x, Fs, 0.050*Fs, 0.025*Fs)

aubio example:

import aubio

source = aubio.source("example.wav")
pitch_o = aubio.pitch("yin", samplerate=source.samplerate)

pitches = []
for frame in source:
    pitch = pitch_o(frame)[0]
    pitches.append(pitch)

Pros of essentia

• More comprehensive library with a wider range of audio analysis algorithms
• Better performance and efficiency, especially for large-scale audio processing
• Supports multiple programming languages (C++, Python, JavaScript)

Cons of essentia

• Steeper learning curve due to its complexity and extensive feature set
• Requires more setup and configuration compared to pyAudioAnalysis
• Less focused on high-level audio analysis tasks

Code comparison

pyAudioAnalysis:

from pyAudioAnalysis import audioBasicIO
from pyAudioAnalysis import audioFeatureExtraction

[Fs, x] = audioBasicIO.read_audio_file("example.wav")
F, f_names = audioFeatureExtraction.stFeatureExtraction(x, Fs, 0.050*Fs, 0.025*Fs)

essentia:

import essentia.standard as es

audio = es.MonoLoader(filename='example.wav')()
w = es.Windowing(type='hann')
spectrum = es.Spectrum()
mfcc = es.MFCC()

for frame in es.FrameGenerator(audio, frameSize=1024, hopSize=512):
    mfcc_bands, mfcc_coeffs = mfcc(spectrum(w(frame)))

Both libraries offer audio analysis capabilities, but essentia provides more flexibility and advanced features at the cost of increased complexity. pyAudioAnalysis is more straightforward for basic audio analysis tasks, while essentia is better suited for more complex and large-scale audio processing projects.

Pros of madmom

• More focused on music information retrieval (MIR) tasks
• Offers deep learning-based algorithms for advanced audio analysis
• Provides specialized tools for beat tracking and tempo estimation

Cons of madmom

• Steeper learning curve due to its more specialized nature
• Less comprehensive documentation compared to pyAudioAnalysis
• Fewer general-purpose audio analysis features

Code Comparison

madmom example:

from madmom.features.beats import RNNBeatProcessor
processor = RNNBeatProcessor()
beats = processor(audio_file)

pyAudioAnalysis example:

from pyAudioAnalysis import audioBasicIO as aIO
from pyAudioAnalysis import ShortTermFeatures as aF
[Fs, x] = aIO.read_audio_file(audio_file)
F, f_names = aF.feature_extraction(x, Fs, 0.050*Fs, 0.025*Fs)

madmom is more specialized for music-related tasks, offering advanced algorithms for beat detection and tempo estimation. It's particularly useful for MIR applications but may have a steeper learning curve.

pyAudioAnalysis provides a broader range of audio analysis features and is generally easier to use for beginners. It's more suitable for general-purpose audio analysis tasks but may lack some of the advanced music-specific functionalities offered by madmom.

Choose madmom for music-focused projects requiring advanced MIR capabilities, and pyAudioAnalysis for general audio analysis tasks or when ease of use is a priority.

Pros of Magenta

• Broader scope, covering music and art generation with machine learning
• More active development and larger community support
• Extensive documentation and tutorials for various use cases

Cons of Magenta

• Steeper learning curve due to its complexity and breadth
• Requires more computational resources for many tasks
• Less focused on audio analysis compared to pyAudioAnalysis

Code Comparison

pyAudioAnalysis example:

from pyAudioAnalysis import audioBasicIO
from pyAudioAnalysis import audioFeatureExtraction
[Fs, x] = audioBasicIO.read_audio_file("example.wav")
F, f_names = audioFeatureExtraction.stFeatureExtraction(x, Fs, 0.050*Fs, 0.025*Fs)

Magenta example:

import note_seq
from note_seq.protobuf import music_pb2

sequence = music_pb2.NoteSequence()
note = sequence.notes.add()
note.start_time = 0
note.end_time = 0.5
note.pitch = 60
note.velocity = 80

Both repositories offer powerful tools for audio and music processing, but they serve different purposes. pyAudioAnalysis focuses on audio feature extraction and classification, while Magenta provides a broader set of tools for music generation and creative applications using machine learning.