Sound Separation
AudioShake leads the field in high-quality source separation, which is typically measured via something called the Signal-to-Distortion (SDR) score. AudioShake has repeatedly demonstrated its ability to achieve the highest SDR scores and set several state of the art benchmarks.
But SDR scores alone are not a good way to measure source separation–it’s quite possible to achieve a high SDR score while producing output that doesn’t sound very good. That’s why we focus even more on building models with the best perceptual quality, and routinely top the lists in third-party evaluations.
But SDR scores alone are not a good way to measure source separation–it’s quite possible to achieve a high SDR score while producing output that doesn’t sound very good. That’s why we focus even more on building models with the best perceptual quality, and routinely top the lists in third-party evaluations.
SDR Scores
The music information retrieval community typically uses something called the Signal-to-Distortion (SDR) score to measure quality, and AudioShake has repeatedly demonstrated its ability to achieve the highest SDR scores. However, Even though we hold many of the state-of-the-art benchmarks, we caution people from solely using SDR score as the best indicator of a high-quality model, because it is quite possible to achieve a high SDR score while the actual results sound poor.
That's why we have developed perceptual metrics that we use for evaluating model performance, and sometimes choose lower-SDR scores models that perform better on different tasks.
With all that said, below is a sample of our stem separation scores for music.
That's why we have developed perceptual metrics that we use for evaluating model performance, and sometimes choose lower-SDR scores models that perform better on different tasks.
With all that said, below is a sample of our stem separation scores for music.
Hear the difference
Models from 10x-400x faster than real-time
Process hours-long files
Process high-resolution (192kHz) files
Process hours-long files
Process high-resolution (192kHz) files
Song: "Future" by Torches
Future
Torches
Torches
SAM Audio
Demucs v4
Spleeter
Transcription Accuracy
To evaluate transcription models, researchers use industry-wide benchmarks that measure word error rate (WER) alongside punctuation and formatting metrics. Alignment is evaluated separately, measuring how closely predicted word and line timings match the audio. At ISMIR, AudioShake’s research team presented a new benchmark based on the JamendoLyrics dataset that captures the finer nuances of written lyrics, called Jam-ALT.
Below are the results of running various transcription and alignment systems against these benchmarks. AudioShake sets the state-of-the-art in both categories — including 95% accuracy on lyric alignment — making it the most accurate system available for transcription and for the word-level timing that synced lyrics, karaoke, and captioning depend on.
Below are the results of running various transcription and alignment systems against these benchmarks. AudioShake sets the state-of-the-art in both categories — including 95% accuracy on lyric alignment — making it the most accurate system available for transcription and for the word-level timing that synced lyrics, karaoke, and captioning depend on.
Get in touch.