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task3_sound_event_detection_in_real_life_audio.yaml
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# ==========================================================
# Flow
# ==========================================================
flow:
initialize: true
extract_features: true
feature_normalizer: true
train_system: true
test_system: true
evaluate_system: true
# ==========================================================
# General
# ==========================================================
general:
development_dataset: TUTSoundEvents_2016_DevelopmentSet
challenge_dataset: TUTSoundEvents_2016_EvaluationSet
overwrite: false # Overwrite previously stored data
# ==========================================================
# Paths
# ==========================================================
path:
data: data/
base: system/baseline_dcase2016_task3/
features: features/
feature_normalizers: feature_normalizers/
models: acoustic_models/
results: evaluation_results/
challenge_results: challenge_submission/task_3_sound_event_detection_in_real_life_audio/
# ==========================================================
# Feature extraction
# ==========================================================
features:
fs: 44100
win_length_seconds: 0.04
hop_length_seconds: 0.02
include_mfcc0: false
include_delta: true
include_acceleration: true
mfcc:
window: hamming_asymmetric # [hann_asymmetric, hamming_asymmetric]
n_mfcc: 20 # Number of MFCC coefficients
n_mels: 40 # Number of MEL bands used
n_fft: 2048 # FFT length
fmin: 0 # Minimum frequency when constructing MEL bands
fmax: 22050 # Maximum frequency when constructing MEL band
htk: false # Switch for HTK-styled MEL-frequency equation
mfcc_delta:
width: 9
mfcc_acceleration:
width: 9
# ==========================================================
# Classifier
# ==========================================================
classifier:
method: gmm # The system supports only gmm
parameters: !!null # Parameters are copied from classifier_parameters based on defined method
classifier_parameters:
gmm:
n_components: 16 # Number of Gaussian components
covariance_type: diag # [diag|full] Diagonal or full covariance matrix
random_state: 0
thresh: !!null
tol: 0.001
min_covar: 0.001
n_iter: 40
n_init: 1
params: wmc
init_params: wmc
# ==========================================================
# Detector
# ==========================================================
detector:
decision_threshold: 160.0
smoothing_window_length: 1.0 # seconds
minimum_event_length: 0.1 # seconds
minimum_event_gap: 0.1 # seconds