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Back in the 1950s, Donald Broadbentidentified a question that is still relevanttoday to the study of how we hear andof the problems suffered by those with ahearing loss: How do we untangle themixtures of sounds that reach our earsand determine which parts of the mix-ture belong to the same source? Hearingresearch in Cambridge has continued toflourish since those early experiments atthe Applied Psychology Unit (APU) – in-cluding the classic experiments on audi-tory filter shapes carried out by RoyPatterson at the APU, and the distin-guished contributions of Brian Moore’sgroup, featured on page 39 of this issue.Here we highlight some of the other ex-

citing research going on in Cambridge, in-cluding that at the APU’s successor, theMRC Cognition & Brain Sciences Unit(CBU). But hearing research in Cam-bridge spans a wide range of groups, in-cluding those based at the Engineering,Psychology, and Physiology departmentsof Cambridge University, and at Adden-brooke’s hospital. The result is a multidis-ciplinary approach that not only probesthe fundamentals of auditory science, butalso addresses issues that are at the heartof the disabilities experienced by users ofhearing aids and of cochlear implants.

How does the healthy auditory systemestimate pitch?Broadbent’s experiments showed that lis-teners use pitch differences betweencompeting speakers to determinewhether or not two speech “formants”come from the same voice. So pitch isvery important when listening to mix-tures of sounds, such as at a cocktailparty (or – even more fun - at a postersession at the BSA conference). But howdoes the brain compute the pitch of asound? It’s been known for a long timethat for a complex sound, such as avowel, we estimate the fundamental fre-quency (pitch) by combining informationfrom different frequency components(harmonics). At the CBU, Hedwig Gockelhas used a subjective tone called HugginsPitch to help identify the neural basis forthis “pitch of the missing fundamental”. AHuggins pitch can be produced by pre-

senting a noise that is exactly the samein each ear, except for a narrow fre-quency region where the noise in thetwo ears are uncorrelated. Because eachear receives just a noise, the Hugginspitch must be derived by regions of thebrain – such as the Medial Superior Olive(MSO) – that compares sound reachingthe two ears. Hedwig showed that thelisteners can readily combine a Hugginspitch with a “regular” harmonic and heara pitch corresponding to the missing fun-damental. This means that the brain mustcompute pitch in a region that receivesinput from the MSO.

Hedwig has also been studying the neuralbasis of pitch perception by measuringthe Frequency Following Response (FFR)– sometimes called the “complex ABR”.The FFR is measured using scalp elec-trodes that record the brainstem re-sponse to sound. Because the FFR oftencontains a periodicity corresponding tothe perceived pitch, it’s often assumedthat it reflects the result of pitch process-ing by the brainstem. However her re-sults – obtained with Bob Carlyon of theCBU and Chris Plack of the University ofManchester – show that this is not thecase. Indeed, by presenting frequencycomponents to opposite ears it’s possibleto produce two sounds with very differ-ent pitches and very similar FFRs, or, con-versely, similar FFRs and different pitches.The research shows that information thatthe brain might use to compute pitch ispresent at the level of the brainstem andcan be recorded using the FFR, but thatthe many reported findings obtainedusing the FFR do not show that pitch isactually extracted at the level of thebrainstem.

A different approach to studying pitchperception comes from Ian Winter’sgroup in the PDN department at Cam-bridge University. By obtaining single cellrecordings his research sheds light on the

The beat goes on: Hearing research in Cambridge

CDAuthors and Correspondence

Bob Carlyon MRCProgramme Leader

E: bob.carlyon@mrc-cbu.cam.ac.uk

David BaguleyConsultant Clinical ScientistCambridge University Hospital NHSFoundation Trust

Matt DaviesProgramme leader, Hearing, Speech and Language GroupCognition and Brain Sciences Unit

Rich TurnerLecturer in Computer Vision and Machine Learning, Department of EngineeringUniversity of Cambridge

Ian WinterSenior LecturerDepartment of PhysiologyDevelopment and NeuroscienceUniversity of Cambridge

neural computations important for pitch.For example, Ian and a new Ph.D. stu-dent, Sami Alsindi, will soon obtain directrecordings from the MSO in order to in-vestigate how the brain processes the in-teraural decorrelation that is necessaryfor the perception of a Huggins pitch.Ian’s group has a long history of, and con-tinued interest in, the neural basis of pitchperception, and has involved a widerange of collaborators including, initially,Roy Patterson and Lutz Wiegrebe andthen latterly with Jesko Verhey, VeronikaNeuert, Bob Carlyon, and Mark Sayles.

Ian’s group has also been studying an-other aspect of auditory scene analysis,relevant to our ability to distinguish sig-nals from background noise - somethingthat has important survival conse-quences such as when detecting a pred-ator (either in the wild, or at the BSAconference). Interestingly backgroundnoise is often not random but slowly fluc-tuates in time and it appears that the au-ditory system has evolved to make useof these slow fluctuations. For example,we are much better at detecting a signalwhen it is accompanied by a fluctuatingnoise than a non-fluctuating noise. How-ever, it is equally important that thesefluctuations are coherent in time acrossdifferent frequency regions of thecochlea. For example, listeners are betterat detecting a tone in a background noisewhen the noise fluctuations are the same

in all frequency regions – a phenomenonknown as comodulation masking release(CMR). Until recently, it was not knownhow the brain processes sound so as totake advantage of these coherent fluctu-ations. Ian’s research has shown thatbrainstem processes may play a pivotalrole, and has identified neurons in thecochlear nucleus whose responses reflectthe CMR experienced by human listen-ers (Figure 1).

Why is pitch perception by cochlear im-plant (CI) users so poor? Can we doanything to help?Poor pitch perception remains one of thechief complaints of CI users, and limitstheir enjoyment of music and their abilityto separate competing voices. Implant

companies are trying to improve the waythat their devices represent pitch infor-mation by implementing new speech-processing algorithms. At the CBU webypass the speech processor and presentsimplified stimuli – such as regular pulsetrains presented to a single electrode –to the CI user. This approach has revealedthat there are limitations inherent to CIusers that limit pitch perception and thatcan’t be overcome by even the latestspeech-processing algorithms. For exam-ple, if we gradually increase the frequencyof a pulse train presented to a CI user,s/he will typically say that the pitch in-creases only up to a rate of about 300Hz. Bob Carlyon and John Deeks havebeen studying the neural basis for this“upper limit” by recording auditory nerveactivity and measuring pitch discrimina-tion with the same subjects and stimuli.They’ve found that even when pitch per-ception is poor, such as at high pulserates, there is plenty of information pres-ent in the auditory nerve. Hence the lim-itation must occur more centrally. Incollaboration with Steve and Julie Bierer(Univ. Washington), Ian Winter and BobCarlyon are planning to probe progres-sively higher stages of the auditory sys-tem to find out more accurately wherethe limitation lies.

CIs usually stimulate the auditorynerve using symmetric pulses,consisting of a positive (anodic)and a negative (cathodic) phasein quick succession. During hisPh.D. at the University of Leuven,co-supervised by Jan Woutersand Bob Carlyon, OlivierMacherey showed that it is theanodic phase that effectivelystimulates the auditory nerves ofCI users. At the CBU Olivier andBob then showed that it’s possi-ble to use this finding to “steer”neural excitation to a more api-cal site than is possible with con-

ventional (symmetric) waveforms. Thisallowed them to increase the range ofpitches that patients could hear, both asa direct result of shifting the “place of ex-citation”, but also because selective stim-ulation of the apex increased the rangeof pulse rates over which pitch continuedto increase.

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Figure 1 A receptive field of a single auditory neuronrecorded in the ventral cochlear nucleus. The red solid linemarks the extent of the excitatory response whilst the solidblue lines mark the boundaries of the inhibitory areas.

Cognition & Brain Sciences Unit (CBU)

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What’s wrong with Auditory BrainstemImplants = and can we do anything tohelp?Some profoundly deaf patients cannotreceive a CI because their auditory nerveis damaged – for example as a result oftumour removal in cases of neurofibro-matosis type 2 (“NF2”). Most of thesepatients have very poor speech percep-tion, and, usually, it is not possible to stim-ulate all electrodes so as to produce asufficiently loud sound without producingundesirable side effects (non-auditorysensations). Together with Colette McKay(previously at the University of Manches-ter ; now at the University of Melbourne)Bob Carlyon and John Deeks have beenprobing the basic psychophysics of howthe auditory brainstem responds to elec-trical stimulation. In some ways, this turnsout to be very different from a CI – forexample, increasing pulse rate from 250to 1000 pps greatly reduces thresholdsin CI users, but has almost no effect forABI users. On the other hand, we’veshown that, like CI users, ABI patients re-spond primarily to the anodic phase of abiphasic pulse. This means that it mightbe possible to extend the range ofpitches heard by ABI users, in the sameway as we’ve shown for CIs.

Does what we know affect how we or-ganise the auditory world?Usually, sound segregation is studied bypsycho-acousticians using very artificialsounds such as sequences of pure tones.This has led to some interesting findingssuch as the fact that, when we presentlisteners with a sequence of tones thatalternate in frequency, they can split intotwo separate “auditory streams”, suchthat listeners cannot hear the compositerhythm formed by the two tones, and arevery poor at detecting differences in therelative timing of the tones in the twostreams. This auditory streaming is ex-ploited by composers of polyphonicmusic to allow the same instrument toplay two separate melodies. However,until recently it wasn’t known whetherthis streaming operates automatically, orwhether it depends on higher-level cog-nitive processes. To study this, Ph.D. stu-dent Alex Billig played listeners repeatingsequences of syllables, such as the word“stem” and the non-word “sten”, and

found that the initial “s” wouldstream off so that the listenerheard a sequence of “s” soundsand another stream comprisingthe remainder of the syllable.(This is an example of the “verbaltransformation effect”, first dis-covered by Richard Warren at theAPU in 1959!). Importantly, heshowed that the tendency for asyllable to stream apart wasgreater for non-words than forwords. This effect of lexicality wasmeasured not only using subjec-tive reports but also using an ob-jective task: when the sequencesstreamed apart it was harder todetect an extra gap inserted be-tween the “s” and the rest of the syllable,and, because words streamed apart lessfrequently than non-words, gap detectionwas better for the words. What thismeans is that auditory sound segregationisn’t a one-way street: streaming doesn’tjust organise the incoming speech intoseparate voices so that we can under-stand what is being said, but is alsostrongly influenced by what we knowabout the language.

Predicting and understanding speechsoundsListeners may use the words that theyknow (and other forms of knowledge)not only to affect sound segregation – asin the study described above – but alsoto predict upcoming speech sounds. Bycomparing their predictions to thesounds that they hear listeners can makeoptimal sense of incoming speech – forinstance, recognising a word like cathe-dral before they have heard more than“cathed…”. Since there is only one wordthat matches this beginning, the finalsounds can be predicted with confi-dence. This ‘predictive coding’ account hasbeen shown by Matt Davis at the CBUto predict brain responses to speech asmeasured using Magnetoencephalogra-phy (MEG). For example, working withPierre Gagnepain and Rik Henson, Mattshowed that hearing an unexpectedword ending (such as “cathedruke”) pro-duces a brain response correlated withprediction error. Predictions are refined– and hence brain responses decrease –when participants are taught that “cathe-

druke” is a real word and given the op-portunity to consolidate their knowledgeovernight.

The results of another MEG study thatare well explained by predictive codingwas run by a CBU PhD student Ediz So-hoglu jointly supervised by Matt Davis,Bob Carlyon and Jonathan Peelle. Listen-ers heard spoken words that were de-graded by noise vocoding in a way thatsimulates speech as transduced by a CI.Depending on the number of channels inthe vocoder – analogous to the elec-trodes in an CI – spoken words soundclearer or more degraded. Words thatare physically clearer produce a larger re-sponse in auditory regions of the brain –particularly in the superior temporalgyrus (STG, a brain area just above theear that responds preferentially tospeech). However, words that soundclearer because listeners are given a writ-ten clue as to which word they are hear-ing (analogous to TV subtitles) produceda smaller response in the STG. This pat-tern of results is well explained by ‘pre-dictive coding’ – speech that soundsclearer can produce an increased re-sponse if it contains additional acousticinformation, whereas speech that is pre-dicted produces less prediction error andhence a reduced brain response. Ed So-hoglu is currently analysing a study ofchanges in the brain response that occuras listeners are taught to make sense ofvocoded speech. This study provides alaboratory analogue of the rehabilitationprocess experienced when a post-lin-

CI patient testing

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gually deafened individual receives acochlear implant and has to learn to hearspeech again. Previous work led by MattDavis has shown how written subtitlescan not only enhance immediate percep-tion of degraded speech, but also sup-port perceptual learning which allows forbetter comprehension even when subti-tles are not available. By understandinghow brain responses for prediction andprediction error support learning mayhelp guide interventions that enhance re-habilitation in CI users.

Auditory scene analysis, inference, andthe statistics of soundsSeveral of the studies described abovereveal the principles that the auditorybrain uses to untangle mixtures ofsounds into their component sources.However, some aspects of DonaldBroadbent's original question still remain

out of reach. For example, we do notknow how the plethora of streamingprinciples believed to be at play act incombination in complex real world envi-ronments – what causes one principle towin out over another? Nor do we under-stand how the brain has arrived at theseprinciples in the first place. Rich Turnerfrom the Computational PerceptionGroup in the Department of Engineeringis investigating a tantalising hypothesisthat may provide the answer to thesequestions. The “inferential” hypothesis

predicts that the auditory brainreasons about the compositionof incoming stimulus based onknowledge of the statistics ofnaturally occurring sounds.

Consider the famous “continu-ity” illusion as an example. Herea tone stops abruptly and is im-mediately followed by a loudnoise burst which is sufficient tohave masked the tone. Con-trary to the physical stimulus,listeners perceive the tone ascontinuing into the noise forabout 100ms. This is consistentwith the idea that they have

learned that tonal sounds in the naturalenvironment tend to vary slowly overtime, and so when the noise burst beginsthe brain “predicts” that the tone is per-sisting. Rich has shown that a computa-tional model, which makes inferencesbased upon the statistics of naturalsounds, reasons in the same way about

the continuity illusion thereforelending support to the hypoth-esis that streaming principlesare intimately related to thestatistics of natural sounds.

Interestingly, the same model ofnatural sounds is excellent atdescribing a class of soundscalled auditory textures. In fact,the model can produce syn-thetic, but natural sounding ver-sions of textures includingrunning water, pattering rain,roaring fire, and howling wind.This is a cute trick - but does ithave practical use? Well, audi-tory textures often contami-

nate sounds of interest like speech, andRich Turner has shown that the statisticalmodel can be used in these cases totease apart the noise from the signal ofinterest. In effect, this process trades offstreaming principles using a knowledgeof the statistics of sounds, and Rich there-fore expects it to shed light on the basisof human perception in complexacoustic environments, thereby taking usone step closer to resolving DonaldBroadbent's foundational question. In ad-dition, Phil Gomersall, a Ph.D. studentfunded by Action on Hearing Loss, isusing Rich’s model to generate soundsthat may be used to alleviate tinnitus incochlear implant users. In a collaborationspanning three Cambridge researchgroups, Phil is jointly supervised by RichTurner, Bob Carlyon, and by David Bagu-ley – a tinnitus expert and head of theaudiology department at Addenbrookeshospital.

Clinical research in CambridgeClinical and translational hearing and bal-ance research is thriving in Cambridge.Collaborations between clinicians atCambridge University Hospitals andbasic research groups in the city includeresearch on tinnitus and cochlear im-plants, as well as projects with Bob Car-lyon on optimising auditory brainstemimplants and CIs, with a focus on poorand mediocre performers with hearingimplants. There are also several currentthemes based primarily at the main hos-pital site. The first of these is the phar-macological treatment of acute tinnitususing an intra-tympanically administeredglutamate antagonist. We are also inves-tigating the impact of single sided deaf-ness and appropriate interventions(including cochlear implants), and specif-ically considering access to music forsuch individuals. A collaboration with ac-ademic Neurology colleagues is investi-gating the MEG imaging of musicalhallucinations, and attempting to identifypatterns of neural activity associatedwith this symptom. All of this research atCambridge University Hospitals is up-held by multi-disciplinary collaborationbetween Audiology, Hearing Implant,Otology and Auditory Neurosciencecolleagues. Sound Lab