Pros of madmom

• More comprehensive audio analysis toolkit with features like beat tracking, onset detection, and chord recognition
• Established project with longer development history and wider adoption in MIR research
• Supports both offline and real-time processing of audio signals

Cons of madmom

• Less focus on GPU acceleration and deep learning integration
• Steeper learning curve due to more complex architecture and broader feature set
• Slower development pace with less frequent updates

Code Comparison

madmom example:

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

nnAudio example:

from nnAudio import Spectrogram
spec_layer = Spectrogram.STFT()
spectrogram = spec_layer(audio_tensor)

madmom provides higher-level audio analysis functions, while nnAudio focuses on efficient spectrogram computation using neural network layers. nnAudio's approach allows for easier integration with deep learning pipelines and GPU acceleration.

Pros of librosa

• Comprehensive audio processing library with a wide range of features
• Well-established and widely used in the audio research community
• Extensive documentation and examples available

Cons of librosa

• CPU-based processing, which can be slower for large-scale operations
• Not optimized for GPU acceleration or deep learning integration

Code Comparison

librosa:

import librosa
y, sr = librosa.load('audio.wav')
stft = librosa.stft(y)
mel_spec = librosa.feature.melspectrogram(y=y, sr=sr)

nnAudio:

from nnAudio import Spectrogram
spec_layer = Spectrogram.STFT()
mel_layer = Spectrogram.MelSpectrogram()
stft = spec_layer(audio_tensor)
mel_spec = mel_layer(audio_tensor)

Key Differences

• nnAudio is designed for GPU acceleration and seamless integration with PyTorch
• librosa offers a broader range of audio processing functions
• nnAudio focuses on spectrogram generation as neural network layers
• librosa provides more flexibility for customizing audio analysis parameters
• nnAudio is better suited for deep learning workflows, while librosa excels in general audio processing tasks

Both libraries have their strengths, and the choice between them depends on the specific requirements of your audio processing project and whether GPU acceleration is necessary.

Pros of essentia

• Comprehensive library with a wide range of audio analysis algorithms
• Supports multiple programming languages (C++, Python, JavaScript)
• Well-documented and actively maintained by a research institution

Cons of essentia

• Steeper learning curve due to its extensive feature set
• Requires more setup and configuration compared to nnAudio
• May be overkill for simple audio processing tasks

Code comparison

nnAudio (PyTorch-based):

from nnAudio import Spectrogram
spec_layer = Spectrogram.STFT()
spec = spec_layer(audio_tensor)

essentia (Python bindings):

import essentia.standard as es
w = es.Windowing(type='hann')
spectrum = es.Spectrum()
spectrogram = es.SpectrumCQ()

Summary

essentia is a comprehensive audio analysis library with support for multiple languages, making it suitable for complex audio processing tasks. However, it may have a steeper learning curve and require more setup than nnAudio. nnAudio, being PyTorch-based, offers a simpler interface for basic audio processing tasks, especially when working with neural networks. The choice between the two depends on the specific requirements of your project and your familiarity with audio processing concepts.

Pros of aubio

• Mature and well-established library with a long history of development
• Supports a wide range of audio analysis tasks beyond spectrograms
• Offers bindings for multiple programming languages

Cons of aubio

• Less focused on neural network-based audio processing
• May have a steeper learning curve for users primarily interested in spectrogram generation
• Potentially slower for certain operations compared to GPU-accelerated alternatives

Code Comparison

aubio:

import aubio
win_s = 512
hop_s = win_s // 2
src = aubio.source('input.wav', samplerate=44100, hop_size=hop_s)
pv = aubio.pvoc(win_s, hop_s)
specgram = []

nnAudio:

import torch
from nnAudio import Spectrogram
spec_layer = Spectrogram.STFT(n_fft=512, hop_length=256)
audio = torch.from_numpy(audio_array)
spectrogram = spec_layer(audio)

Both libraries offer spectrogram generation, but nnAudio is more focused on integrating with neural network workflows using PyTorch, while aubio provides a broader set of audio analysis tools. nnAudio may be more suitable for deep learning projects, while aubio excels in traditional audio processing tasks.

Pros of pedalboard

• Broader audio processing capabilities, including effects like reverb and distortion
• Designed for real-time audio processing, suitable for live applications
• Backed by Spotify, potentially offering better long-term support and updates

Cons of pedalboard

• Less focused on neural network audio processing tasks
• May have a steeper learning curve for users primarily interested in audio analysis

Code Comparison

nnAudio example:

from nnAudio import Spectrogram
spec_layer = Spectrogram.STFT()
spec = spec_layer(audio_tensor)

pedalboard example:

from pedalboard import Pedalboard, Reverb, Distortion
board = Pedalboard([Reverb(), Distortion()])
effected = board(audio_array, sample_rate)

Summary

nnAudio focuses on audio analysis and spectrograms for neural networks, while pedalboard offers a wider range of audio processing tools. nnAudio may be more suitable for researchers working on audio-based machine learning tasks, whereas pedalboard is better suited for general audio manipulation and effects processing. The choice between the two depends on the specific requirements of the project and the user's familiarity with audio processing concepts.