Spaces:

JsonDONSON
/

Machine-Sound-Anomaly-Detector

Sleeping

Machine-Sound-Anomaly-Detector / features /augmentation.py

Upload 3 files

2b668de verified 7 months ago

2.34 kB

"""
===============================================================================
features/augmentation.py — Data Augmentation for Training
===============================================================================
"""

import numpy as np
from config import FREQ_MASK_PARAM, TIME_MASK_PARAM, NOISE_STD, AUGMENT_PROB

AUGMENT_PROB = 0.5         # 50% chance to apply each augmentation independently
FREQ_MASK_PARAM = 24       # Max number of consecutive frequency bins to mask (out of 128)
TIME_MASK_PARAM = 40       # Max number of consecutive time frames to mask 
NOISE_STD = 0.05           # Standard deviation of the Gaussian noise

# note regarding the noise the random noise might push data over the limit we made 
# in the mel spectro gram to be between 0 and 1 so will need to clip

def apply_augmentation(mel_spec):
    """
    Apply data augmentation to a mel spectrogram (training only).
    Notes
    -----
    - This function creates a COPY of the input — the original is not modified.
    - Each call produces a DIFFERENT augmentation due to randomness.
      So each epoch, the same sample looks slightly different → diversity.
    - The augmentation is seeded by numpy's global RNG. For reproducibility
      across runs, set np.random.seed() in the training script.
    """
    augmented = mel_spec.copy()
    n_mels, time_frames = augmented.shape

    # Frequency Masking (SpecAugment) 
    if np.random.random() < AUGMENT_PROB:
        f = np.random.randint(0, FREQ_MASK_PARAM)  # Width of freq mask
        f0 = np.random.randint(0, n_mels - f)      # Starting freq bin
        augmented[f0:f0 + f, :] = 0.0              # Zero out the horizontal band

    # Time Masking (SpecAugment) 
    if np.random.random() < AUGMENT_PROB:
        t = np.random.randint(0, TIME_MASK_PARAM)    # Width of time mask
        t0 = np.random.randint(0, time_frames - t)   # Starting time frame
        augmented[:, t0:t0 + t] = 0.0                # Zero out the vertical block

    # Gaussian Noise Injection 
    if np.random.random() < AUGMENT_PROB:
        noise = np.random.normal(0, NOISE_STD, augmented.shape)
        augmented = augmented + noise
        
        # Safety clip to ensure noise doesn't break the [0, 1] normalization bounds
        augmented = np.clip(augmented, 0.0, 1.0)

    return augmented

	"""
	===============================================================================
	features/augmentation.py — Data Augmentation for Training
	===============================================================================

	"""

	import numpy as np
	from config import FREQ_MASK_PARAM, TIME_MASK_PARAM, NOISE_STD, AUGMENT_PROB

	AUGMENT_PROB = 0.5 # 50% chance to apply each augmentation independently
	FREQ_MASK_PARAM = 24 # Max number of consecutive frequency bins to mask (out of 128)
	TIME_MASK_PARAM = 40 # Max number of consecutive time frames to mask
	NOISE_STD = 0.05 # Standard deviation of the Gaussian noise

	# note regarding the noise the random noise might push data over the limit we made
	# in the mel spectro gram to be between 0 and 1 so will need to clip

	def apply_augmentation(mel_spec):
	"""
	Apply data augmentation to a mel spectrogram (training only).

	Notes
	-----
	- This function creates a COPY of the input — the original is not modified.
	- Each call produces a DIFFERENT augmentation due to randomness.
	So each epoch, the same sample looks slightly different → diversity.
	- The augmentation is seeded by numpy's global RNG. For reproducibility
	across runs, set np.random.seed() in the training script.

	"""
	augmented = mel_spec.copy()
	n_mels, time_frames = augmented.shape

	# Frequency Masking (SpecAugment)
	if np.random.random() < AUGMENT_PROB:
	f = np.random.randint(0, FREQ_MASK_PARAM) # Width of freq mask
	f0 = np.random.randint(0, n_mels - f) # Starting freq bin
	augmented[f0:f0 + f, :] = 0.0 # Zero out the horizontal band

	# Time Masking (SpecAugment)
	if np.random.random() < AUGMENT_PROB:
	t = np.random.randint(0, TIME_MASK_PARAM) # Width of time mask
	t0 = np.random.randint(0, time_frames - t) # Starting time frame
	augmented[:, t0:t0 + t] = 0.0 # Zero out the vertical block

	# Gaussian Noise Injection
	if np.random.random() < AUGMENT_PROB:
	noise = np.random.normal(0, NOISE_STD, augmented.shape)
	augmented = augmented + noise

	# Safety clip to ensure noise doesn't break the [0, 1] normalization bounds
	augmented = np.clip(augmented, 0.0, 1.0)

	return augmented