Neural audio quality estimation based on normalized quantization error
AssigneesFraunhofer-Gesellschaft zur Foerderung der angewandten Forschung e.V., Friedrich-Alexander-Universitaet Erlangen-Nuernberg, in Vertretung des Freistaates Bayern
InventorsMohammed Modar HALIMEH, Emanuel A.P. HABETS, Matteo TORCOLI, Philipp GRUNDHUBER, Harald FUCHS, Yannik GREWE
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Abstract
A deep neural audio quality estimation module includes a deep neural encoder, a vector quantizer, and a quality calculation module. The quality calculation module estimating the audio quality of an audio signal based on a normalized quantization error of the vector quantizer for the audio signal.
Claims24
A method performed by one or more apparatuses, the method comprising:
- obtaining an input audio waveform that comprises a sequence of audio samples,
- using a pre-trained encoder neural network to generate, based on the input audio waveform, a sequence of feature vectors representing the sequence of audio samples using multiple features,
- for each feature vector of the sequence of feature vectors:
- determining a respective coded representation of the respective feature vector using vector quantization and further determining one or more indicators indicative of a respective quantization error associated with the respective coded representation of the respective feature vector,
- for each feature vector of the sequence of feature vectors:
- determining a respective quality factor based on one or more relationships between the one or more indicators indicative of the respective quantization error and one or more respective normalization values.
An apparatus, comprising a processor and a memory, the process of being configured to load program code from the memory and to execute the program code, the processor, upon executing the program code, being configured to:
- obtain an input audio waveform that comprises a sequence of audio samples,
- use a pre-trained encoder neural network to generate, based on the input audio waveform, a sequence of feature vectors representing the sequence of audio samples using multiple features,
- for each feature vector of the sequence of feature vectors:
- determine a respective coded representation of the respective feature vector using vector quantization and further determine one or more indicators indicative of a respective quantization error associated with the respective coded representation of the respective feature vector,
- for each feature vector of the sequence of feature vectors:
- determine a respective quality factor based on one or more relationships between the one or more indicators indicative of the respective quantization error and one or more respective normalization values.
A method of training an audio quality estimation module, the audio quality estimation module comprising an encoder neural network for determining feature vectors based on input vectors, a vector quantizer for determining coded representations of the feature vectors, and a decoder neural network for decoding the coded representations of the feature vectors, thereby obtaining output vectors,
- wherein the method comprises:
- adjusting weights of the encoder neural network, the vector quantizer, and the decoder neural network using one or more losses, at least one of the one or more losses taking into account a differences between the multiple input vectors and the respectively associated multiple output vectors,
- wherein the multiple input vectors at least comprise non-clean-speech audio waveforms.
A deep neural audio quality estimation module comprising a deep neural encoder, a vector quantizer, and a quality calculation module, the quality calculation module estimating the audio quality of an audio signal based on a normalized quantization error of the vector quantizer for the audio signal.
Dependencies are inferred from the claim text.