LoopyLuci Audio Processing with Deep Learning
Applying deep learning to audio — from spectrograms and mel-frequency features through audio classification, source separation, and audio generation.
When to Use
- Classifying audio (speech, music, environmental sounds)
- Separating audio sources (vocals from instruments)
- Audio generation and enhancement
- Feature extraction for audio ML pipelines
Audio Feature Pipeline
import torch
import torchaudio
import torchaudio.functional as F
class AudioProcessor:
"""Extract features from audio for deep learning."""
@staticmethod
def mel_spectrogram(waveform: torch.Tensor, sample_rate: int = 16000) -> torch.Tensor:
mel = torchaudio.transforms.MelSpectrogram(
sample_rate=sample_rate,
n_fft=1024, hop_length=512, n_mels=128,
)
spec = mel(waveform)
return torchaudio.transforms.AmplitudeToDB()(spec)
@staticmethod
def mfcc(waveform: torch.Tensor, sample_rate: int = 16000, n_mfcc: int = 13):
mfcc = torchaudio.transforms.MFCC(sample_rate, n_mfcc=n_mfcc)
return mfcc(waveform)
class AudioClassifier(nn.Module):
"""CNN for audio classification on mel-spectrograms."""
def __init__(self, n_classes: int, n_mels: int = 128):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=2)
self.pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(64, n_classes)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.relu(self.conv2(x))
x = self.pool(x).squeeze(-1).squeeze(-1)
return self.fc(x)
Verification Checklist
- Audio format standardized (sample rate, channels, bit depth)
- Feature extraction (mel-spectrogram, MFCC) tested
- Data augmentation (speed, pitch, noise, SpecAugment)
- Model architecture suitable for audio (CNN, CRNN, Transformer)
- Audio classification/regression metrics (accuracy, MSE, MOS)
- Real-time inference latency benchmarked