Microphone array signal processing for robot audition

Robot audition for humanoid robots interacting naturally with humans in an unconstrained real-world environment is a hitherto unsolved challenge. The recorded microphone signals are usually distorted by background and interfering noise sources (speakers) as well as room reverberation. In addition, the movements of a robot and its actuators cause ego-noise which degrades the recorded signals significantly. The movement of the robot body and its head also complicates the detection and tracking of the desired, possibly moving, sound sources of interest. This paper presents an overview of the concepts in microphone array processing for robot audition and some recent achievements

Electroacoustic and Behavioural Evaluation of Hearing Aid Digital Signal Processing Features

Modern digital hearing aids provide an array of features to improve the user listening experience. As the features become more advanced and interdependent, it becomes increasingly necessary to develop accurate and cost-effective methods to evaluate their performance. Subjective experiments are an accurate method to determine hearing aid performance but they come with a high monetary and time cost. Four studies that develop and evaluate electroacoustic hearing aid feature evaluation techniques are presented. The first study applies a recent speech quality metric to two bilateral wireless hearing aids with various features enabled in a variety of environmental conditions. The study shows that accurate speech quality predictions are made with a reduced version of the original metric, and that a portion of the original metric does not perform well when applied to a novel subjective speech quality rating database. The second study presents a reference free (non-intrusive) electroacoustic speech quality metric developed specifically for hearing aid applications and compares its performance to a recent intrusive metric. The non-intrusive metric offers the advantage of eliminating the need for a shaped reference signal and can be used in real time applications but requires a sacrifice in prediction accuracy. The third study investigates the digital noise reduction performance of seven recent hearing aid models. An electroacoustic measurement system is presented that allows the noise and speech signals to be separated from hearing aid recordings. It is shown how this can be used to investigate digital noise reduction performance through the application of speech quality and speech intelligibility measures. It is also shown how the system can be used to quantify digital noise reduction attack times. The fourth study presents a turntable-based system to investigate hearing aid directionality performance. Two methods to extract the signal of interest are described. Polar plots are presented for a number of hearing aid models from recordings generated in both the free-field and from a head-and-torso simulator. It is expected that the proposed electroacoustic techniques will assist Audiologists and hearing researchers in choosing, benchmarking, and fine-tuning hearing aid features

User-Centered Translation in Website Localization - Overall Usability of the Finnish Country Site of Hotels.com

Nykyajan globalisoituvassa maailmassa yritysten on tärkeää tarjota Internet-sivut kotimaisten asiakkaiden lisäksi kansainvälisille markkinoille. Tämä tarve korostuu erityisesti matkailualan yrityksillä. Jotta sivusto tavoittaisi asiakkaita mahdollisimman kattavasti ympäri maailman, pelkkä englanninkielinen sivusto ei riitä, vaan tarvitaan myös lokalisoituja eli kohdekieleen ja -kulttuuriin kotoutettuja versioita, maasivustoja. Lokalisointi kattaa useita eri toimintoja kuten tekstin kääntämisen sekä muiden elementtien muokkaamisen kohdekulttuurin vaatimusten mukaiseksi. Menestyksekkään lokalisoinnin taustalla on käytettävyyden huomioiminen joka vaiheessa. Tässä pro gradu -tutkielmassa arvioitiin globaalin matkailualan brändin Hotels.comin Suomen maasivujen kokonaiskäytettävyyttä. Erityisesti tutkittiin Suomen maasivuston käännöksiä Tytti Suojasen, Kaisa Koskisen ja Tiina Tuomisen hiljattain kehittämän käyttäjäkeskeisen kääntämisen (UCT) näkökulmasta, mutta myös erilaisten kulttuuristen elementtien kotouttamista sekä sivuston yleistä käytettävyyttä. Tutkielmassa sovellettiin Jakob Nielsenin alun perin teknisen viestinnän tarkoituksiin kehittämää heuristista arviointia, joka on myös yksi UCT-metodeista. Materiaalia tarkasteltiin heuristisesti nettisivujen globalisoinnin, lokalisoinnin, käännösten sekä yleisen käytettävyyden näkökulmista. Jokaiselle näkökulmalle luotiin omat heuristiikat, jotka pohjautuvat John Yunkerin verkkoglobalisoinnin parhaat käytänteet -listaan, mm. Bert Esselinkin, Minako O’Haganin ja Carmen Mangironin näkemyksiin lokali-soinnista, Suojanen ym.:n käyttäjäkeskeisen kääntämisen konseptiin sekä Nielsenin perustavanlaatuisiin näkemyksiin käytettävyydestä. Ennakko-oletuksen mukaisesti heuristinen arviointi osoittautui hyödylliseksi nettisivujen käännösten arvioinnissa. Heuristiikkojen avulla löydettiin todellisia käytettävyysongelmia sekä käännösten että muiden tutkittujen aspektien alueelta. Löydetyt käytettävyysongelmat olivat pääsääntöisesti melko pieniä, vain muutama oli vakavuusluokitukseltaan suuri ja vaatisi pikaista korjausta. Suomen maasivusto osoittautui siis kokonaisuudessaan käytettävyydeltään hyväksi sivustoksi. Löydetyn kaltaiset ongelmat voitaisiin kuitenkin välttää käyttämällä heuristiikkoja jo lokalisoinnin alkuvaiheessa. Niiden käyttö iteratiivisesti puolestaan mahdollistaa ongelmien korjauksen päivitysten yhteydessä. Tutkielman tuloksista on hyötyä paitsi Hotels.comin lokalisoinnista vastaaville tahoille myös muille matkailualan maakohtaisia verkkosivuja lokalisoiville.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Microphone Array Speech Enhancement Via Beamforming Based Deep Learning Network

In general, in-car speech enhancement is an application of the microphone array speech enhancement in particular acoustic environments. Speech enhancement inside the moving cars is always an interesting topic and the researchers work to create some modules to increase the quality of speech and intelligibility of speech in cars. The passenger dialogue inside the car, the sound of other equipment, and a wide range of interference effects are major challenges in the task of speech separation in-car environment. To overcome this issue, a novel Beamforming based Deep learning Network (Bf-DLN) has been proposed for speech enhancement. Initially, the captured microphone array signals are pre-processed using an Adaptive beamforming technique named Least Constrained Minimum Variance (LCMV). Consequently, the proposed method uses a time-frequency representation to transform the pre-processed data into an image. The smoothed pseudo-Wigner-Ville distribution (SPWVD) is used for converting time-domain speech inputs into images. Convolutional deep belief network (CDBN) is used to extract the most pertinent features from these transformed images. Enhanced Elephant Heard Algorithm (EEHA) is used for selecting the desired source by eliminating the interference source. The experimental result demonstrates the effectiveness of the proposed strategy in removing background noise from the original speech signal. The proposed strategy outperforms existing methods in terms of PESQ, STOI, SSNRI, and SNR. The PESQ of the proposed Bf-DLN has a maximum PESQ of 1.98, whereas existing models like Two-stage Bi-LSTM has 1.82, DNN-C has 1.75 and GCN has 1.68 respectively. The PESQ of the proposed method is 1.75%, 3.15%, and 4.22% better than the existing GCN, DNN-C, and Bi-LSTM techniques. The efficacy of the proposed method is then validated by experiments

Digital Signal Processing Research Program

Contains table of contents for Section 2, an introduction and reports on seventeen research projects.U.S. Navy - Office of Naval Research Grant N00014-91-J-1628Vertical Arrays for the Heard Island Experiment Award No. SC 48548Charles S. Draper Laboratories, Inc. Contract DL-H-418472Defense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-89-J-1489Rockwell Corporation Doctoral FellowshipMIT - Woods Hole Oceanographic Institution Joint ProgramDefense Advanced Research Projects Agency/U.S. Navy - Office of Naval Research Grant N00014-90-J-1109Lockheed Sanders, Inc./U.S. Navy - Office of Naval Research Contract N00014-91-C-0125U.S. Air Force - Office of Scientific Research Grant AFOSR-91-0034AT&T Laboratories Doctoral ProgramU.S. Navy - Office of Naval Research Grant N00014-91-J-1628General Electric Foundation Graduate Fellowship in Electrical EngineeringNational Science Foundation Grant MIP 87-14969National Science Foundation Graduate FellowshipCanada Natural Sciences and Engineering Research CouncilLockheed Sanders, Inc

VR/AR and hearing research: current examples and future challenges

A well-known issue in clinical audiology and hearing research is the level of abstraction of traditional experimental assessments and methods, which lack ecological validity and differ significantly from real-life experiences, often resulting in unreliable outcomes. Attempts to deal with this matter by, for example, performing experiments in real-life contexts, can be problematic due to the difficulty of accurately identifying control-specific parameters and events. Virtual and augmented reality (VR/AR) have the potential to provide dynamic and immersive audiovisual experiences that are at the same time realistic and highly controllable. Several successful attempts have been made to create and validate VR-based implementations of standard audiological and linguistic tests, as well as to design procedures and technologies to assess meaningful and ecologically-valid data. Similarly, new viewpoints on auditory perception have been provided by looking at hearing training and auditory sensory augmentation, aiming at improving perceptual skills in tasks such as speech understanding and sound-source localisation. In this contribution, we bring together researchers active in this domain. We briefly describe experiments they have designed, and jointly identify challenges that are still open and common approaches to tackle the

Deep Spoken Keyword Spotting:An Overview

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to the paradigm shift introduced by deep learning a few years ago. This has allowed the rapid embedding of deep KWS in a myriad of small electronic devices with different purposes like the activation of voice assistants. Prospects suggest a sustained growth in terms of social use of this technology. Thus, it is not surprising that deep KWS has become a hot research topic among speech scientists, who constantly look for KWS performance improvement and computational complexity reduction. This context motivates this paper, in which we conduct a literature review into deep spoken KWS to assist practitioners and researchers who are interested in this technology. Specifically, this overview has a comprehensive nature by covering a thorough analysis of deep KWS systems (which includes speech features, acoustic modeling and posterior handling), robustness methods, applications, datasets, evaluation metrics, performance of deep KWS systems and audio-visual KWS. The analysis performed in this paper allows us to identify a number of directions for future research, including directions adopted from automatic speech recognition research and directions that are unique to the problem of spoken KWS