Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Digital processing of speech signals

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

When building a unified vision system or gradually adding new capabilities to a system, the usual assumption is that training data for all tasks is always available. However, as the number of tasks grows, storing and retraining on such data becomes infeasible. A new problem arises where we add new capabilities to a Convolutional Neural Network (CNN), but the training data for its existing capabilities are unavailable. We propose our Learning without Forgetting method, which uses only new task data to train the network while preserving the original capabilities. Our method performs favorably compared to commonly used feature extraction and fine-tuning adaption techniques and performs similarly to multitask learning that uses original task data we assume unavailable. A more surprising observation is that Learning without Forgetting may be able to replace fine-tuning with similar old and new task datasets for improved new task performance.

Learning without Forgetting

This paper describes an adaptive multi-rate wideband (AMR-WB) speech codec proposed for the GSM system and also for the evolving third generation (3G) mobile speech services. The speech codec is based on SB-CELP (splitband-code-excited linear prediction) with five modes operating bit rates from 24 kbit/s down to 9.1 kbit/s. The respective channel coding schemes are based on RSC (recursive systematic code) and UEP (unequal error protection). Both, source and channel codec are designed as homogenous as possible to guarantee robust transmission on current and future mobile radio channels.

/pdf/a-candidate-proposal-for-a-3gpp-adaptive-multi-rate-wideband-3x79rgrily.pdf

A candidate proposal for a 3GPP adaptive multi-rate wideband speech codec

Estimating time-frequency domain masks for speech enhancement using deep learning approaches has recently become a popular field in research. In this paper, we propose a novel components loss (CL) for the training of neural networks for speech enhancement. During the training process, the proposed CL offers separate control over suppression of the noise component and preservation of the speech component. We illustrate the potential of the proposed CL by example of a convolutional neural network (CNN) for mask-based speech enhancement. We show improvement in almost all employed instrumental quality metrics over the baseline losses, which comprises the conventional mean squared error (MSE) loss and also perceptual evaluation of speech quality (PESQ) loss. On average, more than 0.3 dB higher SNR improvement and an at least 0.1 points higher PESQ score on the speech component are obtained. In addition to that, a more naturally sounding residual noise and a consistently best PESQ on the enhanced speech is obtained. All improvements are more distinct at low SNR conditions.

Using Separate Losses for Speech and Noise in Mask-Based Speech Enhancement

In the context of noise reduction algorithms, three instrumental measures are of major interest: the speech component quality, the level of noise attenuation, and noise distortion in terms of musical tones. As several proposals are made for the first two, the amount of musical tones is commonly still subjectively evaluated. Recent exploration of the log-kurtosis ratio for instrumentally measuring musical tones has led to white box test methodologies requiring specific information about the particular noise reduction algorithm. In this paper we propose a simple yet robust instrumental musical tones measurement, which is applicable to arbitrary unknown noise reduction systems, i.e., a black box measurement. A subjective listening test has been conducted to verify the proposed instrumental measure. Our measurement methodology has been proposed as part of an ITU-T Recommendation in Study Group 12, FG CarCOM.

Black box measurement of musical tones produced by noise reduction systems

The Interspeech 2020 Deep Noise Suppression (DNS) Challenge focuses on evaluating low-latency single-channel speech enhancement algorithms under realistic test conditions. Our contribution to the challenge is a method for joint dereverberation and denoising based on complex spectral mask estimation using a fully convolutional recurrent network (FCRN) which relies on a convolutional LSTM layer for temporal modeling. Since the effects of reverberation and noise on perceived speech quality can differ notably, a multi-target loss for controlling the weight on desired dereverberation and denoising is proposed. In the crowdsourced subjective P.808 listening test conducted by the DNS Challenge organizers, the proposed method shows a significant overall improvement of 0.43 MOS points over the DNS Challenge baseline and ranks amongst the top-3 submissions for both realtime and non-realtime tracks of the challenge.

INTERSPEECH 2020 Deep Noise Suppression Challenge: A Fully Convolutional Recurrent Network (FCRN) for Joint Dereverberation and Denoising.

Determining the individuality of handwriting in ancient manuscripts is an important aspect of the manuscript analysis process. Automatic identification of writers in historical manuscripts can support historians to gain insights into manuscripts with missing metadata such as writer name, period, and origin. In this paper writer classification and retrieval approaches for multi-page documents in the context of historical manuscripts are presented. The main contribution is a learning-based rejection strategy which utilizes writer retrieval and support vector machines for rejecting a decision if no corresponding writer can be found for a query manuscript. Experiments using different feature extraction methods demonstrate the abilities of our proposed methods. A dedicated data set based on a publicly available database of historical Arabic manuscripts was used and the experiments show promising results.

https://www.cs.bgu.ac.il/~abedas/pdfs/icfhr_writer_id_CR.pdf

Tim Fingscheidt

Papers

A candidate proposal for a 3GPP adaptive multi-rate wideband speech codec

Using Separate Losses for Speech and Noise in Mask-Based Speech Enhancement

Black box measurement of musical tones produced by noise reduction systems

INTERSPEECH 2020 Deep Noise Suppression Challenge: A Fully Convolutional Recurrent Network (FCRN) for Joint Dereverberation and Denoising.

Document Writer Analysis with Rejection for Historical Arabic Manuscripts