These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Scale-invariant signal-to-distortion ratio improvement (SI-SDRi)
Scale-invariant signal-to-distortion ratio improvement (SI-SDRi) is a metric used to evaluate the performance of speech and audio source separation algorithms. It measures the improvement in the quality of the separated signal over the original mixed signal, taking into account the scale-invariance of audio signals.
The SI-SDRi is defined as the ratio between the scale-invariant signal-to-distortion ratio (SI-SDR) of the separated signal and the SI-SDR of the mixed signal, both measured in decibels (dB). The SI-SDR is a variant of the signal-to-distortion ratio (SDR) that takes into account the scaling and permutation of the separated sources. It is computed as the ratio between the energy of the reference source and the energy of the distortion signal, after aligning the sources using the permutation invariant training (PIT) method.
The SI-SDRi is a more robust and meaningful metric than the traditional SDR, especially for scenarios where the scaling and permutation of the sources are unknown or arbitrary. A higher SI-SDRi value indicates a better separation performance, with a value of 0 dB indicating no improvement and higher values indicating greater improvement.
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