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Score-Informed Source Separation for Musical Audio Recordings [An overview]
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Automatic Quality Estimation for ASR System Combination
Recognizer Output Voting Error Reduction (ROVER) has been widely used for
system combination in automatic speech recognition (ASR). In order to select
the most appropriate words to insert at each position in the output
transcriptions, some ROVER extensions rely on critical information such as
confidence scores and other ASR decoder features. This information, which is
not always available, highly depends on the decoding process and sometimes
tends to over estimate the real quality of the recognized words. In this paper
we propose a novel variant of ROVER that takes advantage of ASR quality
estimation (QE) for ranking the transcriptions at "segment level" instead of:
i) relying on confidence scores, or ii) feeding ROVER with randomly ordered
hypotheses. We first introduce an effective set of features to compensate for
the absence of ASR decoder information. Then, we apply QE techniques to perform
accurate hypothesis ranking at segment-level before starting the fusion
process. The evaluation is carried out on two different tasks, in which we
respectively combine hypotheses coming from independent ASR systems and
multi-microphone recordings. In both tasks, it is assumed that the ASR decoder
information is not available. The proposed approach significantly outperforms
standard ROVER and it is competitive with two strong oracles that e xploit
prior knowledge about the real quality of the hypotheses to be combined.
Compared to standard ROVER, the abs olute WER improvements in the two
evaluation scenarios range from 0.5% to 7.3%
Adversarial Semi-Supervised Audio Source Separation applied to Singing Voice Extraction
The state of the art in music source separation employs neural networks
trained in a supervised fashion on multi-track databases to estimate the
sources from a given mixture. With only few datasets available, often extensive
data augmentation is used to combat overfitting. Mixing random tracks, however,
can even reduce separation performance as instruments in real music are
strongly correlated. The key concept in our approach is that source estimates
of an optimal separator should be indistinguishable from real source signals.
Based on this idea, we drive the separator towards outputs deemed as realistic
by discriminator networks that are trained to tell apart real from separator
samples. This way, we can also use unpaired source and mixture recordings
without the drawbacks of creating unrealistic music mixtures. Our framework is
widely applicable as it does not assume a specific network architecture or
number of sources. To our knowledge, this is the first adoption of adversarial
training for music source separation. In a prototype experiment for singing
voice separation, separation performance increases with our approach compared
to purely supervised training.Comment: 5 pages, 2 figures, 1 table. Final version of manuscript accepted for
2018 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP). Implementation available at
https://github.com/f90/AdversarialAudioSeparatio
Structured Dropout for Weak Label and Multi-Instance Learning and Its Application to Score-Informed Source Separation
Many success stories involving deep neural networks are instances of
supervised learning, where available labels power gradient-based learning
methods. Creating such labels, however, can be expensive and thus there is
increasing interest in weak labels which only provide coarse information, with
uncertainty regarding time, location or value. Using such labels often leads to
considerable challenges for the learning process. Current methods for
weak-label training often employ standard supervised approaches that
additionally reassign or prune labels during the learning process. The
information gain, however, is often limited as only the importance of labels
where the network already yields reasonable results is boosted. We propose
treating weak-label training as an unsupervised problem and use the labels to
guide the representation learning to induce structure. To this end, we propose
two autoencoder extensions: class activity penalties and structured dropout. We
demonstrate the capabilities of our approach in the context of score-informed
source separation of music
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