1 0
`
`1
`
`Complete Sound and Speech Recognition System for Health Smart Homes:
`Application to the Recognition of Activities of Daily Living
`
`Complete Sound and Speech Recognition System
`for Health Smart Homes: Application to the
`Recognition of Activities of Daily Living
`
`Michel Vacher1, Anthony Fleury2, François Portet1, Jean-François Serignat1,
`Norbert Noury2
`1Laboratoire d’Informatique de Grenoble, GETALP team, Université de Grenoble
`France
`2Laboratory TIMC-IMAG, AFIRM team, Université de Grenoble
`France
`
`1. Introduction
`
`Recent advances in technology have made possible the emergence of Health Smart Homes
`(Chan et al., 2008) designed to improve daily living conditions and independence for the
`population with loss of autonomy. Health smart homes are aiming at assisting disabled and
`the growing number of elderly people which, according to the World Health Organization
`(WHO), is forecasted to reach 2 billion by 2050. Of course, one of the first wishes of this pop-
`ulation is to be able to live independently as long as possible for a better comfort and to age
`well. Independent living also reduces the cost to society of supporting people who have lost
`some autonomy. Nowadays, when somebody is loosing autonomy, according to the health
`system of her country, she is transferred to a care institution which will provide all the neces-
`sary supports. Autonomy assessment is usually performed by geriatricians, using the index
`of independence in Activities of Daily Living (ADL) (Katz & Akpom, 1976), which evaluates
`the person’s ability to realize different activities of daily living (e.g., doing a meal, washing,
`going to the toilets . . . ) either alone, or with a little or total assistance. For example, the AG-
`GIR grid (Autonomie Gérontologie Groupes Iso-Ressources) is used by the French health system.
`Seventeen activities including ten discriminative (e.g., talking coherently, orientating himself,
`dressing, going to the toilets...) and seven illustrative (e.g., transports, money management,
`...) are graded with an A (the task can be achieved alone, completely and correctly), a B (the
`task has not been totally performed without assistance or not completely or not correctly) or
`a C (the task has not been achieved). Using these grades, a score is computed and, according
`to the scale, a geriatrician can deduce the person’s level of autonomy to evaluate the need for
`medical or financial support.
`Health Smart Home has been designed to provide daily living support to compensate some
`disabilities (e.g., memory help), to provide training (e.g., guided muscular exercise) or to de-
`tect harmful situations (e.g., fall, gas not turned off). Basically, an health smart home contains
`sensors used to monitor the activity of the inhabitant. The sensors data is analyzed to detect
`the current situation and to execute the appropriate feedback or assistance. One of the first
`steps to achieve these goals is to detect the daily activities and to assess the evolution of the
`
`APPLE 1051
`
`1
`
`

`

`2
`
`Recent Advances in Biomedical Engineering
`
`monitored person’s autonomy. Therefore, activity recognition is an active research area (Albi-
`nali et al., 2007; Dalal et al., 2005; Duchêne et al., 2007; Duong et al., 2009; Fleury, 2008; Moore
`& Essa, 2002) but, despite this, it has still not reached a satisfactory performance nor led to
`a standard methodology. One reason is the high number of flat configurations and available
`sensors (e.g., infra-red sensors, contact doors, video cameras, RFID tags, etc.) which may not
`provide the necessary information for a robust identification of ADL. Furthermore, to reduce
`the cost of such an equipment and to enable interaction (i.e., assistance) the chosen sensors
`should serve not only to monitor but also to provide feedback and to permit direct orders.
`One of the modalities of choice is the audio channel. Indeed, audio processing can give infor-
`mation about the different sounds in the home (e.g., object falling, washing machine spinning,
`door opening, foot step . . . ) but also about the sentences that have been uttered (e.g., distress
`situations, voice commands). Moreover, speaking is the most natural way for communication.
`A person, who cannot move after a fall but being concious has still the possibility to call for
`assistance while a remote controller may be unreachable.
`In this chapter, we present AUDITHIS— a system that performs real-time sound and speech
`analysis from eight microphone channels — and its evaluation in different settings and exper-
`imental conditions. Before presenting the system, some background about health smart home
`projects and the Habitat Intelligent pour la Santé of Grenoble is given in section 2. The related
`work in the domain of sound and speech processing in Smart Home is introduced in section 3.
`The architecture of the AUDITHIS system is then detailed in section 4. Two experimentations
`performed in the field to validate the detection of distress keywords and the noise suppres-
`sion are then summarised in section 5. AUDITHIS has been used in conjunction with other
`sensors to identify seven Activities of Daily Living. To determine the usefulness of the audio
`information for ADL recognition, a method based on feature selection techniques is presented
`in section 6. The evaluation has been performed on data recorded in the Health Smart Home
`of Grenoble. Both data and evaluation are detailed in section 7. Finally, the limits and the
`challenges of the approach in light of the evaluation results are discussed in section 8.
`
`2. Background
`
`Health smart homes have been designed to provide ambient assisted living. This topic is
`supported by many research programs around the world because ambient assisted living is
`supposed to be one of the many ways to aid the growing number of people with loss of au-
`tonomy (e.g., weak elderly people, disabled people . . . ). Apart from supporting daily living,
`health smart homes constitute a new market to provide services (e.g., video-conferencing,
`tele-medicine, etc.). This explains the involvement of the major telecommunication compa-
`nies. Despite these efforts, health smart home is still in its early age and the domain is far
`from being standardised (Chan et al., 2008). In the following section, the main projects in this
`field — focusing on the activity recognition — are introduced. The reader is referred to (Chan
`et al., 2008) for an extensive overview of smart home projects. The second section is devoted
`to the Health Smart Home of the TIMC-IMAG laboratory which served for the experiments
`described further in this chapter.
`
`2.1 Related Health Smart Home Projects
`To be able to provide assistance, health smart homes need to perceive the environment —
`through sensors — and to infer the current situation. Recognition of activities and distress
`situations are generally done by analyzing the evolution of indicators extracted from the sen-
`sors raw signals. A popular trend is to use as many as possible sensors to acquire the most
`
`2
`
`

`

`Complete Sound and Speech Recognition System for Health Smart Homes:
`Application to the Recognition of Activities of Daily Living
`
`3
`
`information. An opposite direction is to use the least number of sensors as possible to reduce
`the cost of the smart home. For instance, the Edelia company1 evaluates the quantity of wa-
`ter used per day. A model is built from these measurements and in case of high discrepancy
`between the current water use and the model, an alert to the relatives of the inhabitant is gen-
`erated. Similar work has been launched by Zojirushi Corporation2 which keeps track of the
`use of the electric water boiler to help people stay healthy by drinking tea (which is of par-
`ticular importance in Japan). In an hospital environment, the Elite Care project (Adami et al.,
`2003) proposed to detect the bedtime and wake-up hours to adapt the care of patients with
`Alzheimer’s disease.
`These projects focus on only one sensor indicator but most of the research projects includes
`several sensors to estimate the ‘model’ of the lifestyle of the person. The model is generally
`estimated by data mining techniques and permits decision being made from multisource data.
`Such smart homes are numerous. For instance, the project House_n from the Massachusetts
`Institute of Technology, includes a flat equipped with hundreds of sensors (Intille, 2002). These
`sensors are used to help performing the activities of daily living, to test Human-Machine
`Interfaces, to test environment controller or to help people staying physically and mentally
`active. This environment has been designed to easily assess the interest of new sensors (e.g.,
`RFID, video camera, etc.). A notable project, The Aware Home Research Initiative (Abowd et al.,
`2002) by the Georgia Institute of Technology, consists in a two-floor home. The ground floor
`is devoted to an elderly person who lives in an independent manner whereas the upper floor
`is dedicated to her family. This family is composed of a children mentally disabled and his
`parents who raise him while they work full-time. This house is equipped with motion and
`environmental sensors, video cameras (for fall detection and activity recognition (Moore &
`Essa, 2002) and short-term memory help (Tran & Mynatt, 2003)) and finally RFID tags to find
`lost items easily. Both floors are connected with flat screens to permit the communication of
`the two generations. The AILISA (LeBellego et al., 2006) and PROSAFE (Bonhomme et al.,
`2008) projects have monitored the activities of the person with presence infra-red sensors to
`raise alarms in case of abnormal situations (e.g., changes in the level of activities). Within the
`PROSAFE project, the ERGDOM system controls the comfort of the person inside the flat (i.e.,
`temperature, light...).
`Regarding the activity detection, although most of the many researches related to health smart
`homes is focused on sensors, network and data sharing (Chan et al., 2008), a fair number of
`laboratories started to work on reliable Activities of Daily Living (ADL) detection and clas-
`sification using Bayesian (Dalal et al., 2005), rule-based (Duong et al., 2009; Moore & Essa,
`2002), evidential fusion (Hong et al., 2008), Markovian (Albinali et al., 2007; Kröse et al., 2008),
`Support Vector Machine (Fleury, 2008), or ensemble of classifiers (Albinali et al., 2007) ap-
`proaches. For instance, (Kröse et al., 2008) learned models to recognize two activities: ‘going to
`the toilets’ and ‘exit from the flat’. (Hong et al., 2008) tagged the entire fridge content and other
`equipments in the flat to differentiate the activities of preparing cold or hot drinks from hy-
`giene. Most of these approaches have used Infra-red sensors, contact doors, videos, RFID tags
`etc. But, to the best of our knowledge, only few studies include audio sensors (Intille, 2002)
`and even less have assessed what the important features (i.e. sensors) for robust classification
`of activities are (Albinali et al., 2007; Dalal et al., 2005). Moreover, these projects considered
`only few activities while many daily living activities detection is required for autonomy as-
`sessment. Our approach was to identify seven activities of daily living that will be useful for
`
`1 www.edelia.fr/
`2 www.zojirushi-world.com/
`
`3
`
`

`

`4
`
`Recent Advances in Biomedical Engineering
`
`the automatic evaluation of autonomy, and then to equip our Health Smart Home with the
`most relevant sensors to learn models of the different activities (Portet et al., 2009). The next
`section details the configuration of health smart home.
`
`2.2 The TIMC-IMAG’s Health Smart Home
`Since 1999, the TIMC-IMAG laboratory in Grenoble set-up, inside the faculty of medicine of
`Grenoble, a flat of 47m2 equipped with sensing technology. This flat is called HIS from the
`French denomination: Habitat Intelligent pour la Santé (i.e., Health Smart Home). The sensors
`and the flat organization are presented in Figure 1. It includes a bedroom, a living-room, a
`corridor, a kitchen (with cupboards, fridge. . . ), a bathroom with a shower and a cabinet. It
`has been firstly equipped with presence infra-red sensors, in the context of the AILISA project
`(LeBellego et al., 2006) and served as prototype for implementation into two flats of elderly
`persons and into hospital suites of elderly people in France. Important features brought by
`the infra-red sensors have been identified such as mobility and agitation (Noury et al., 2006)
`(respectively the number of transitions between sensors and the number of consecutive detec-
`tions on one sensor) which are related to the health status of the person (Noury et al., 2008).
`The HIS equipment has been further complemented with several sensors to include:
`(cid:89) presence infra-red sensors (PIR), placed in each room to sense the location of the person in
`the flat;
`(cid:89) door contacts, for the recording of the use of some furniture (fridge, cupboard and
`dresser);
`(cid:89) microphones, set in each room to process sounds and speech; and
`(cid:89) large angle webcams, that are placed only for annotation purpose.
`
`Phone
`
`Presence Infra−
`Red sensors
`
`Microphones
`
`Large Angle
`Webcamera
`
`Temperature and
`
`Hygrometry sensors
`
`Doors contacts
`
`Fig. 1. The Health Smart Home of the TIMC-IMAG Laboratory in Grenoble
`
`4
`
`

`

`Complete Sound and Speech Recognition System for Health Smart Homes:
`Application to the Recognition of Activities of Daily Living
`
`5
`
`The cost of deployment of such installation is reduced by using only the sensors that are the
`most informative. This explains the small number of sensors compared to other smart homes
`(Intille, 2002). The technical room contains 4 standard computers which receive and store,
`in real time, the information from the sensors. The sensors are connected with serial port
`(contact-doors), USB port (webcams), wireless receiver (PIRs) or through an analog acquisi-
`tion board (microphones). Except for the microphones these connections are available on ev-
`ery (even low-cost) computer. These sensors were chosen to enable the recognition of activities
`of daily living, such as sleeping, preparing and having a breakfast dressing and undressing,
`resting, etc. The information that can be extracted from these sensors and the activities they
`are related to are summarised in Table 5 presented in section 7.
`
`It is important to note that this flat represents an hostile environment for information acquisi-
`tion similar to the one that can be encountered in real home. This is particularly true for the
`audio information. For example, we have no control on the sounds that are measured from
`the exterior (e.g., the flat is near the helicopter landing strip of the local hospital). Moreover,
`there is a lot of reverberation because of the 2 important glazed areas opposite to each other
`in the living room. The sound and speech recognition system presented in section 4 has been
`tested in laboratory and gave an average Signal to Noise Ratio of 27dB in-lab. In the HIS, this
`fell to 12dB. Thus, the signal processing and learning methods that are presented in the next
`sections have to address the challenges of activity recognition in such a noisy environment.
`
`3. State of the Art in the Context of Sound and Speech Analysis
`
`Automatic sound and speech analysis are involved in numerous fields of investigation due
`to an increasing interest for automatic monitoring systems. Sounds can be speech, music,
`songs or more generally sounds of the everyday life (e.g., dishes, step,. . . ). This state of the art
`presents firstly the sound and speech recognition domains and then details the main applica-
`tions of sound and speech recognition in smart home context.
`
`3.1 Sound Recognition
`Sound recognition is a challenge that has been explored for many years using machine
`learning methods with different techniques (e.g., neural networks, learning vector quantiza-
`tions,. . . ) and with different features extracted depending on the technique (Cowling & Sitte,
`2003). It can be used for many applications inside the home, such as the quantification of
`water use (Ibarz et al., 2008) but it is mostly used for the detection of distress situations. For
`instance, (Litvak et al., 2008) used microphones to detect a special distress situation: the fall.
`An accelerometer and a microphone are both placed on the floor. Mixing sound and vibration
`of the floor allowed to detect fall of the occupant of the room. (Popescu et al., 2008) used
`two microphones for the same purpose, using Kohonen Neural Networks. Out of a context of
`distress situation detection, (Chen et al., 2005) used HMM with the Mel-Frequency Cepstral
`Coefficients (MFCC) to determine the different uses of the bathroom (in order to recognize
`sequences of daily living). (Cowling, 2004) applied the recognition of non-speech sounds as-
`sociated with their direction, with the purpose of using these techniques in an autonomous
`mobile surveillance robot.
`
`3.2 Speech Recognition
`Human communication by voice appears to be so simple that we tend to forget how variable
`a signal speech is. In fact, spoken utterances even of the same text are characterized by large
`
`5
`
`

`

`6
`
`Recent Advances in Biomedical Engineering
`
`differences that depend on context, speaking style, the speaker’s dialect, the acoustic environ-
`ment... Even identical texts spoken by the same speaker can show sizable acoustic differences.
`Automatic methods of speech recognition must be able to handle this large variability in a
`fault-free fashion and thus the progress in speech processing are not as fast as hoped at the
`time of the early work in this field.
`The phoneme duration, the fundamental frequency (melody) and the Fourier analysis have
`been used for studying phonograph recordings of speech in 1906. The concept of short-term
`representation of speech, where individual feature vectors are computed from short (10-20
`ms) semi-stationary segments of the signal, were introduced during the Second World War.
`This concept led to a spectrographic representation of the speech signal and to underline the
`importance of the formants as carriers of linguistic information. The first recognizer used a
`resonator tuned to the vicinity of the first formant vowel region to trigger an action when a
`loud sound were pronounced. This knowledge-based approach were abandoned by the first
`spoken digit recognizer in 1952 (Davis et al., 1952). (Rabiner & Luang, 1996) published the
`scaling algorithm for the Forward-Backward method of training of Hidden Markov Model
`recognizers and at this time modern general-purpose speech recognition systems are gener-
`ally based on HMMs as far as the phonemes are concerned. Models of the targeted language
`are often used. A Language model is a collection of constraints on the sequence of words
`acceptable on a given language and may be adapted to a particular application. The speci-
`ficities of a recognizer are related to its adaptation to a unique speaker or to a large variety of
`speakers, and to its capacities of accepting continuous speech, and small or large vocabularies.
`Many computer softwares are nowadays able to transcript documents on a computer from
`speech that is uttered at normal pace (for the person) and at normal loud in front of a mi-
`crophone connected to the computer. This technique necessitates a learning phase to adapt
`the acoustic models to the person. That is done from a given set of sentences uttered by the
`speaker the first time he used the system. Dictation systems are capable of accepting very
`large vocabularies, more than ten thousand words. Another kind of application aims to rec-
`ognize a small set of commands, i.e. for home automation purpose or on a vocal server (of an
`answering machine for instance). This can be done without a speaker adapted learning step
`(that would be too complicated to set-up). Document transcription and command recognition
`use speech recognition but have to face different problems in their implementation. The first
`application needs to be able to recognize, with the smallest number of mistakes, a large num-
`ber of words. For the second application, the number of words is lower, but the conditions
`are worst. Indeed, the use of speech recognition to enter a text on a computer will be done
`with a good microphone, well placed (because often associated to the headphone) and with
`relatively stable conditions of noise on the measured signal. In the second application, the mi-
`crophone could be, for instance, the one of a cell phone, that will be associated to a low-pass
`filter to reduce the transmissions on the network, and the use could be done in every possible
`conditions (e.g., in a train with a baby crying next to the person).
`More general applications are for example related to the context of civil safety. (Clavel et al.,
`2007) studied the detection and analysis of abnormal situations through fear-type acoustic
`manifestations. Two kinds of application will be presented in the continuation of this section:
`the first one is related to people aids and the second one to home automation.
`
`3.3 Speech and Sound Recognition Applied to People Aids
`Speech and sound recognition have been applied to the assistance to the person. For example,
`based on a low number of words, France Telecom Research and Development worked on a
`
`6
`
`

`

`Complete Sound and Speech Recognition System for Health Smart Homes:
`Application to the Recognition of Activities of Daily Living
`
`7
`
`pervasive scarf that can be useful to elderly or dependant people (with physical disabilities
`for instance) in case of problem. It allows to call, easily (with vocal or tactile commands) a
`given person (previously registered) or the emergencies.
`Concerning disabled or elderly people, (Fezari & Bousbia-Salah, 2007) have demonstrated the
`feasibility to control a wheel chair using a given set of vocal commands. This kind of com-
`mands uses existing speech recognition engines adapted to the application. In the same way,
`Renouard et al. (2003) worked on a system with few commands able to adapt continuously to
`the voice of the person. This system is equipped with a memory that allows the training of a
`reject class.
`Finally, speech recognition can be used to facilitate elderly people access to new technologies.
`For example, Kumiko et al. (2004) aims at assisting elderly people that are not familiar with
`keyboards through the use of vocal commands. Anderson et al. (1999) proposed the speech
`recognition of elderly people in the context of information retrieval in document databases.
`
`3.4 Application of Speech and Sound Recognition in Smart Homes
`Such recognition of speech and sound can be integrated into the home for two applications:
`(cid:89) Home automation,
`(cid:89) Recognition of distress situations.
`For home automation, (Wang et al., 2008) proposed a system based on sound classification,
`this allows them to help or to automatize tasks in the flat. This system is based on a set of
`microphones integrated into the ceiling. Classification is done with Support Vector Machines
`from the MFCC coefficients of the sounds.
`Recognition of distress situations may be achieved through sound or speech analysis; a dis-
`tress situation being recognized when some distress sentences or key words are uttered, or
`when some sounds are emitted in the flat like glass breaking, screams or object falling. This
`was explored by (Maunder et al., 2008) which constructed a database of sounds of daily life
`acquired by two microphones in a kitchen. They tried to differentiate sounds like phone,
`dropping a cup, dropping a spoon, etc. using Gaussian Mixture Models. (Harma et al., 2005)
`collected sounds in an office environment and tried unsupervised algorithms to classify the
`sounds of daily life at work. Another group, (Istrate et al., 2008), aimed at recognizing the
`distress situations at home in embedded situations using affordable material (with classical
`audio sound cards and microphones).
`On another direction, researches have been engaged to model the dialogue of an automated
`system with elderly people (Takahashi et al., 2003). The system performs voice synthesis,
`speech recognition, and construction of a coherent dialogue with the person. This kind of
`research have application in robotics, where the aim is then to accompany the person and
`reduce his loneliness.
`Speech and sound analyses are quite challenging because of the recording conditions. Indeed,
`the microphone is almost never placed near the speaker or embedded, but often set in the
`ceiling. Surrounding noise and sound reverberation can make the recognition very difficult.
`Therefore, speech and sound recognition have to face different kind of problems. Thus a signal
`processing adapted to the recording conditions is requested. Moreover, automatic speech
`recognition necessitates acoustic models (to identify the different phonemes) and languages
`models (recognition of words) adapted to the situation. Elderly people tends to have voice
`characteristics different from the active population (Wilpon & Jacobsen, 1996). (Baba et al.,
`2004) constructed specifically acoustic models for this target population to asses the usefulness
`of such adaptation.
`
`7
`
`

`

`8
`
`Recent Advances in Biomedical Engineering
`
`Our work consists in a complete sound recognition system to identify the different sounds in
`the flat in order to recognize the currently performed activity of daily living, associated to a
`speech recognition system in French to search for distress keywords inside the signal mea-
`sured. The implementation and test of this complete system is described in the next sections.
`
`4. The AUDITHIS and RAPHAEL Systems
`
`The term AUDITHIS is built from the names audit and audition, and the acronym HIS (Habitat
`Intelligent pour la Santé - Health Smart Home) and the merger of audio and audit, because the
`system aims at sound and speech analysis in a health smart home. Therefore, AUDITHIS is
`able to analyze, in real-time, information from eight microphones placed at different location
`of a smart home. Figure 2 depicts the general organization of the AUDITHIS audio analysis
`system and its interaction with the Autonomous Speech Recognizer RAPHAEL.
`
`8 microphones
`
`Acquisition and Preprocessing
`(SNR estimation, Noise cancelation)
`
`AuditHIS Analysis System
`
`Set Up Module
`
`Scheduler Module
`
`Detection,
`Simultaneous
`events
`management
`
`Sound
`
`Sound Classifier
`
`Segmentation
`
`Speech
`
`Keyword Extraction,
`Message Formatting
`
`Autonomous
`Speech
`Recognizer
`(RAPHAEL)
`
`XML Output
`
`Fig. 2. Architecture of the AUDITHIS and RAPHAEL systems
`
`Both systems are running in real-time as independent applications on the same GNU/Linux
`operating system and they are synchronized through a file exchange protocol. Each of the 8
`microphones is connected to an analog input channel of the acquisition board. Each sound
`is processed independently and sucessively by the different modules thanks to a queuing
`management protocol:
`1. Data Acquisition and preprocessing, which is in charge of signal acquisition, SNR es-
`timation, noise cancellation;
`2. Detection, which estimates the beginning and end of a sound to analyse and manage
`the simultaneous audio events;
`3. Segmentation, which classifies each audio event as being speech or sound of daily liv-
`ing;
`4. Sound classification or Speech Recognition (RAPHAEL), which determines which
`class of sound or which phrase has been uttered; and
`5. Message Formatting.
`
`8
`
`

`

`Complete Sound and Speech Recognition System for Health Smart Homes:
`Application to the Recognition of Activities of Daily Living
`
`9
`
`These modules run as independent threads synchronized by a scheduler. The following sec-
`tions detail each of the modules.
`
`4.1 Data Acquisition and preprocessing
`Data acquisition is operated on the 8 input channels simultaneously at a 16 kHz sampling
`rate by the first module. Data of each channel is stored in a buffer and processed sequentially
`and separately. Noise level is also evaluated by this module to assess the Signal to Noise
`Ratio (SNR) of each acquired sound. The SNR of each audio signal is very important for the
`decision system to estimate the reliability of the corresponding analysis output. Moreover,
`noise suppression techniques are incorporated in this module in order to suppress on the fly
`the noise emitted by known sources like TV or radio; this part of the module is described in
`section 4.2.
`
`4.2 Known Source Noise Suppression
`Listening to the radio and watching TV are very frequent everyday activities; this can seri-
`ously disturb a sound and speech recognizer. Because of that, sound and speech analysis
`must solve two problems: firstly, sounds or speech emitted by the person in the flat can be
`altered by the loudspeaker and badly recognized, and secondly, radio and TV sounds will be
`analyzed as well although their information is not relevant. It will be then mandatory to take
`into account the fact that the radio or the TV is up to suppress this noise or to exploit the re-
`sulting information in an other way. Sound x(n) emitted by a loudspeaker in the health smart
`home is a noise source that will be altered by the room acoustics depending on the position of
`the microphone in the room. The resulting noise y(n) of this alteration may be expressed by a
`convolution product in the time domain (Equation 1), h being the impulse response and n the
`discrete time.
`
`y(n) = h(n) ∗ x(n)
`
`(1)
`
`This noise is then superposed to the interesting signal e(n) emitted in the room: speech uttered
`by the person or everyday life sound. The signal recorded by the microphone is then y(n) =
`e(n) + h(n) ∗ x(n). Various methods were developed in order to cancel the noise (Michaut &
`Bellanger, 2005), some methods attempt to obtain h(n) an estimation of the impulse response
`of the room in order to remove the noise as shown on Figure 3. The resulting output is given
`in Equation 2.
`
`v(n) = e(n) + y(n) − y(n) = e(n) + h(n) ∗ x(n) −
`
`h(n) ∗ x(n)
`
`(2)
`
`These methods may be divided into 2 classes: Least Mean Square (LMS) and Recursive Least
`Square (RLS) methods. Stability and convergence properties are studied in (Michaut & Bel-
`langer, 2005). The Multi-delay Block Frequency Domain (MDF) algorithm is an implementa-
`tion of the LMS algorithm in the frequency domain (Soo & Pang, 1990). In echo cancellation
`systems, the presence of audio signal e(n) (double-talk) tends to make the adaptive filter di-
`verge. To prevent this problem, robust echo cancellers require adjustment of the learning
`rate to take the presence of double talk in the signal into account. Most echo cancellation
`algorithms attempt to explicitly detect double-talk but this approach is not very successful,
`especially in presence of a stationary background noise. A new method (Valin & Collings,
`2007) was proposed by the authors of the library, where the misalignment is estimated in
`closed-loop based on a gradient adaptive approach; this closed-loop technique is applied to
`the block frequency domain (MDF) adaptive filter.
`
`9
`
`

`

`10
`
`Recent Advances in Biomedical Engineering
`
`TV or Radio
`
`x(n)
`
`Speech or Sound
`
`Room Acoustic
`h(n)
`
`y(n)
`
`e(n)
`
`Noisy Signal
`
`Adaptive Filter
`h(n)^
`^
`y(n)
`
`v(n)
`
`Output
`
`Fig. 3. Echo Cancellation System used for Noise Suppression
`
`The echo cancellation technique used introduces a specific noise into the v(n) signal and a
`post-filtering is requested. This algorithm is implemented in the SPEEX library under GPL Li-
`cense (Valin, 2007) for echo cancellation system. The method implemented in this library is the
`Minimum Mean Square Estimator Short-Time Amplitude Spectrum Estimator (MMSE-STSA)
`presented in (Ephraim & Malah, 1984). The STSA estimator is associated to an estimation of
`the a priori SNR. The formulated hypothesis are following:
`(cid:89) added noise is Gaussian, stationary and the spectral density is known;
`(cid:89) an estimation of the speech spectrum is available;
`(cid:89) spectral coefficients are Gaussian and statistically independents;
`(cid:89) the phase of the Discrete Fourier Transform follows a uniform distribution law and is
`amplitude independent.
`Some improvements are added to the SNR estimation (Cohen & Berdugo, 2001) and a psycho-
`acoustical approach for post-filtering (Gustafsson et al., 2004) is implemented. The purpose
`of this post-filter is to attenuate both the residual echo remaining after an imperfect echo
`cancellation and the noise without introducing ‘musical noise’ (i.e. randomly distributed, time-
`variant spectral peaks in the residual noise spectrum as spectral subtraction or Wiener rule
`does (Vaseghi, 1996)). The post-filter is implemented in the frequency domain, which basically
`means that the spectrum of the input signal is multiplied by weighting coefficients. Their
`weighted values are chosen by taking into account auditory masking. Noise is inaudible if
`it is t