Loading ivas_processing_scripts/processing/preprocessing_2.py +2 −17 Original line number Diff line number Diff line Loading @@ -114,9 +114,6 @@ class Preprocessing2(Processing): def add_background_noise( self, audio_object: audio.Audio, in_meta, logger ) -> np.ndarray: # range for random delay max_delay = int(2400000 * audio_object.fs / 48000) if self.background_noise.get("background_noise_path"): if not self.background_noise.get("background_noise_path").exists(): raise ValueError( Loading @@ -136,10 +133,6 @@ class Preprocessing2(Processing): # if noise is too short raise error if len(noise_object.audio) < len(audio_object.audio): raise ValueError("Background noise too short for audio signal") if len(noise_object.audio) - max_delay < len(audio_object.audio): raise ValueError( "Background noise may be to short for audio signal when considering the random delay" ) # measure loudness of audio signal based on output format tmp_object = audio.fromtype(self.out_fmt) Loading @@ -163,16 +156,8 @@ class Preprocessing2(Processing): # compute desired loudness of background noise loudness_noise = loudness_signal - self.background_noise["snr"] # apply random delay and cut signal rand_delay = random_seed( range=(1, max_delay), master_seed=self.background_noise["master_seed"], prerun_seed=self.background_noise["seed_delay"], hexa=False, ) noise_object.audio = delay( noise_object.audio, delay=-rand_delay, samples=True, fs=noise_object.fs )[: len(audio_object.audio)] # cut noise signal noise_object.audio = noise_object.audio[: len(audio_object.audio)] # scale background noise to desired loudness based on output format logger.debug( Loading Loading
ivas_processing_scripts/processing/preprocessing_2.py +2 −17 Original line number Diff line number Diff line Loading @@ -114,9 +114,6 @@ class Preprocessing2(Processing): def add_background_noise( self, audio_object: audio.Audio, in_meta, logger ) -> np.ndarray: # range for random delay max_delay = int(2400000 * audio_object.fs / 48000) if self.background_noise.get("background_noise_path"): if not self.background_noise.get("background_noise_path").exists(): raise ValueError( Loading @@ -136,10 +133,6 @@ class Preprocessing2(Processing): # if noise is too short raise error if len(noise_object.audio) < len(audio_object.audio): raise ValueError("Background noise too short for audio signal") if len(noise_object.audio) - max_delay < len(audio_object.audio): raise ValueError( "Background noise may be to short for audio signal when considering the random delay" ) # measure loudness of audio signal based on output format tmp_object = audio.fromtype(self.out_fmt) Loading @@ -163,16 +156,8 @@ class Preprocessing2(Processing): # compute desired loudness of background noise loudness_noise = loudness_signal - self.background_noise["snr"] # apply random delay and cut signal rand_delay = random_seed( range=(1, max_delay), master_seed=self.background_noise["master_seed"], prerun_seed=self.background_noise["seed_delay"], hexa=False, ) noise_object.audio = delay( noise_object.audio, delay=-rand_delay, samples=True, fs=noise_object.fs )[: len(audio_object.audio)] # cut noise signal noise_object.audio = noise_object.audio[: len(audio_object.audio)] # scale background noise to desired loudness based on output format logger.debug( Loading