dcase_util.processors.OpenL3ExtractorProcessor

class dcase_util.processors.OpenL3ExtractorProcessor(fs=44100, hop_length_samples=None, hop_length_seconds=0.02, model=None, input_repr='mel256', content_type='music', embedding_size=6144, center=True, batch_size=32, verbose=False, **kwargs)

Constructor

Parameters

fsint

Sampling rate of the incoming signal.

hop_length_samplesint

Hop length in samples. Default value None

hop_length_secondsfloat

Hop length in seconds. Default value 0.02

modelkeras.models.Model or None

Loaded model object. If a model is provided, then input_repr, content_type, and embedding_size will be ignored. If None is provided, the model will be loaded using the provided values of input_repr, content_type and embedding_size. Default value None

input_repr“linear”, “mel128”, or “mel256”

Spectrogram representation used for model. Ignored if model is a valid Keras model. Default value “mel256”

content_type“music” or “env”

Type of content used to train the embedding model. Ignored if model is a valid Keras model. Default value “music”

embedding_size6144 or 512

Embedding dimensionality. Ignored if model is a valid Keras model. Default value 6144

centerbool

If True, pads beginning of signal so timestamps correspond to center of window. Default value True

batch_sizeint

Batch size used for input to embedding model Default value 32

verbosebool

If True, prints verbose messages. Default value False

__init__(fs=44100, hop_length_samples=None, hop_length_seconds=0.02, model=None, input_repr='mel256', content_type='music', embedding_size=6144, center=True, batch_size=32, verbose=False, **kwargs)

Constructor

Parameters

fsint

Sampling rate of the incoming signal.

hop_length_samplesint

Hop length in samples. Default value None

hop_length_secondsfloat

Hop length in seconds. Default value 0.02

modelkeras.models.Model or None

Loaded model object. If a model is provided, then input_repr, content_type, and embedding_size will be ignored. If None is provided, the model will be loaded using the provided values of input_repr, content_type and embedding_size. Default value None

input_repr“linear”, “mel128”, or “mel256”

Spectrogram representation used for model. Ignored if model is a valid Keras model. Default value “mel256”

content_type“music” or “env”

Type of content used to train the embedding model. Ignored if model is a valid Keras model. Default value “music”

embedding_size6144 or 512

Embedding dimensionality. Ignored if model is a valid Keras model. Default value 6144

centerbool

If True, pads beginning of signal so timestamps correspond to center of window. Default value True

batch_sizeint

Batch size used for input to embedding model Default value 32

verbosebool

If True, prints verbose messages. Default value False

Methods

__init__([fs, hop_length_samples, ...])	Constructor
delimiter([exclude_delimiters])	Use csv.sniffer to guess delimiter for CSV file
detect_file_format([filename])	Detect file format from extension
empty()	Check if file is empty
exists()	Checks that file exists
extract(y)	Extract features for the audio signal.
get_file_information()	Get file information, filename
get_processing_chain_item()	Get processing chain item with current Processor data.
is_package([filename])	Determine if the file is compressed package.
load([filename])	Load file
log([level])	Log container content
process([data, store_processing_chain])	Extract features
save([filename])	Save file
show([mode, indent, visualize])	Print container content
to_html([indent])	Get container information in a HTML formatted string
to_string([ui, indent])	Get container information in a string
validate_format()	Validate file format

Attributes

bytes	File size in bytes
description	Extractor description
input_type	Input data type
label	Extractor label
logger	Logger instance
md5	Checksum for file.
output_type	Output data type
valid_formats	Valid file formats