EP2660814A1 - Adaptives Ausgleichssystem - Google Patents

Adaptives Ausgleichssystem Download PDF

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Publication number
EP2660814A1
EP2660814A1 EP12166906.3A EP12166906A EP2660814A1 EP 2660814 A1 EP2660814 A1 EP 2660814A1 EP 12166906 A EP12166906 A EP 12166906A EP 2660814 A1 EP2660814 A1 EP 2660814A1
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Prior art keywords
speech
signal
curve
equalization coefficients
module
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EP12166906.3A
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English (en)
French (fr)
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EP2660814B1 (de
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Phillip Alan Hetherington
Xueman Li
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2236008 Ontario Inc
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QNX Software Systems Ltd
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Priority to EP12166906.3A priority Critical patent/EP2660814B1/de
Priority to CA2814434A priority patent/CA2814434C/en
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0316—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude
    • G10L21/0364—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude for improving intelligibility
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0264—Noise filtering characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques

Definitions

  • This application relates to sound processing and, more particularly, to adaptive equalization of speech signals.
  • a speech signal may be adversely impacted by acoustical or electrical characteristics of the acoustical environment or the electrical audio path associated with the speech signal.
  • the in-car acoustics or microphone characteristics may have a significant detrimental impact on the sound quality or intelligibility of a speech signal transmitted to a remote party.
  • Figure 1 illustrates an adaptive equalization system.
  • Figure 2 illustrates the functionality of the adaptive equalization system of Figure 1 .
  • Figure 3 illustrates one implementation of a subband processing filterbank.
  • Figure 4 is a graph illustrating one implementation of a signal power estimate and a background noise estimate of a speech signal.
  • Figure 5 is a graph illustrating one implementation of a band importance function.
  • Figure 6 is a graph illustrating two possible long-term average speech curve templates.
  • an adaptive equalization system that improves the intelligibility of a speech signal.
  • the system may automatically adjust the spectral shape of the speech signal to improve speech intelligibility.
  • Equalization techniques such as parametric or graphic equalization have long been implemented in audio products to improve sound quality.
  • an equalization curve is often tuned for a specific environment based on experience or to a particular target, but then usually remains unchanged during production or real-time use.
  • the equalizer is adapted based on a target shape. This system attempts to automatically compensate for deficiencies in the audio path, which makes the output speech more pleasing and intelligible even in the presence of noise.
  • the system may achieve this increase in intelligibility without requiring a voicing decision and without requiring advanced knowledge of the clean speech and the noise level.
  • the system may be implemented in real-time applications where only noisy speech is available.
  • Figure 1 illustrates a system that includes an audio signal source 102, an adaptive equalization system 104, and an audio signal output 106.
  • the adaptive equalization system 104 receives an input speech signal from the audio signal source 102, processes the signal, and outputs an improved version of the input signal to the audio signal output 106.
  • the output signal received by the audio signal output 106 may be more intelligible to a listener than the input signal received by the adaptive equalization system 104.
  • the audio signal source 102 may be a microphone, an incoming communication system channel, a pre-processing system, or another signal input device.
  • the audio signal output 106 may be a loudspeaker, an outgoing communication system channel, a speech recognition system, a post-processing system, or any other output device.
  • the adaptive equalization system 104 includes a computer processor 108 and a memory device 110.
  • the computer processor 108 may be implemented as a central processing unit (CPU), microprocessor, microcontroller, application specific integrated circuit (ASIC), or a combination of other type of circuits.
  • the computer processor is a digital signal processor ("DSP") including a specialized microprocessor with an architecture optimized for the fast operational needs of digital signal processing.
  • DSP digital signal processor
  • the digital signal processor may be designed and customized for a specific application, such as an audio system of a vehicle or a signal processing chip of a mobile communication device (e.g., a phone or tablet computer).
  • the memory device 110 may include a magnetic disc, an optical disc, RAM, ROM, DRAM, SRAM, Flash and/or any other type of computer memory.
  • the memory device 110 is communicatively coupled with the computer processor 108 so that the computer processor 108 can access data stored on the memory device 110, write data to the memory device 110, and execute programs and modules stored on the memory device 110.
  • the memory device 110 includes one or more data storage areas 112 and one or more programs.
  • the data and programs are accessible to the computer processor 108 so that the computer processor 108 is particularly programmed to implement the adaptive equalization functionality of the system.
  • the programs may include one or more modules executable by the computer processor 108 to perform the desired function.
  • the program modules may include a subband processing module 114, a signal power calculation module 116, a background noise level estimation module 118, a speech intelligibility measurement module 120, a spectral shape adjustment module 122, a normalization module 124, and an adaptive equalization module 126.
  • the memory device 110 may also store additional programs, modules, or other data to provide additional programming to allow the computer processor 108 to perform the functionality of the adaptive equalization system 104.
  • the described modules and programs may be parts of a single program, separate programs, or distributed across several memories and processors. Furthermore, the programs and modules, or any portion of the programs and modules, may instead be implemented in hardware.
  • Figure 2 is a flow chart illustrating the functionality of the adaptive equalization system of Figure 1 .
  • the functionality of Figure 2 may be achieved by the computer processor 108 accessing data from data storage 112 of Figure 1 and by executing one or more of the modules 114 - 126 of Figure 1 .
  • the processor 108 may execute the subband processing module 114 at steps 202 and 222, the signal power calculation module 116 at step 204, the background noise level estimation module 118 at step 206, the speech intelligibility measurement module 120 at step 208, the spectral shape adjustment module 122 at step 210, the normalization module 124 at step 212, and the adaptive equalization module 126 at steps 214, 216, 218, and 220.
  • Any of the modules or steps described herein may be combined or divided into a smaller or larger number of steps or modules than what is shown in Figures 1 and 2 .
  • the adaptive equalization system may begin its signal processing sequence in Figure 2 with subband analysis at step 202.
  • the system may receive an input speech signal that includes speech content, noise content, or both.
  • a subband filter processes the input signal to extract frequency information of the input signal.
  • the subband filter may be accomplished by various methods, such as a Fast Fourier Transform ("FFT"), critical filter bank, octave filter bank, or one-third octave filter bank.
  • FFT Fast Fourier Transform
  • the subband analysis at step 202 may include a frequency based transform, such as by a Fast Fourier Transform.
  • the subband analysis at step 202 may include a time based filterbank.
  • the time based filterbank may be composed of a bank of overlapping bandpass filters, where the center frequencies have non-linear spacing such as octave, 3rd octave, bark, mel, or other spacing techniques.
  • Figure 3 illustrates the filter shapes of one implementation of a subband processing filterbank. As shown in Figure 3 , the bands may be narrower at lower frequencies and wider at higher frequencies.
  • the lowest and highest filters may be shelving filters so that all the components may be resynthesized to essentially recreate the same input signal when no processing has been applied.
  • a frequency based transform may use essentially the same filter shapes applied after transformation of the signal to create the same non-linear spacing or subbands. The frequency based transform may also use a windowed add/overlap analysis.
  • the subband processing at step 202 outputs a set of subband signals represented as X n,k , which is the kth subband at time n.
  • the system receives the subband signals and determines the subband average signal power of each subband.
  • the subband average signal power output from step 204 is represented as X n,k .
  • IIR Infinite Impulse Response
  • the coefficient ⁇ is a fixed value.
  • the coefficient ⁇ may be set at a fixed level of 0.9, which results in a relatively high amount of smoothing. Other higher or lower fixed values are also possible depending on the desired amount of smoothing.
  • the coefficient ⁇ may be a variable value. For example, the system may decrease the value of the coefficient ⁇ during times when a lower amount of smoothing is desired, and increase the value of the coefficient ⁇ during times when a higher amount of smoothing is desired.
  • the subband signal is smoothed, filtered, and/or averaged.
  • the amount of smoothing may be constant or variable.
  • the signal is smoothed in time.
  • frequency smoothing may be used.
  • the system may include some frequency smoothing when the subband filters have some frequency overlap.
  • the amount of smoothing may be variable in order to exclude long stretches of silence into the average or for other reasons.
  • the power analysis processing at step 204 outputs a smoothed magnitude/power of the input signal in each subband.
  • the system receives the subband signals and estimates a subband background noise level for each subband.
  • the subband average signal power output from step 206 is represented as B n,k .
  • the background noise level is calculated using the background noise estimation techniques disclosed in U.S. Patent No. 7,844,453 , which is incorporated herein by reference, except that in the event of any inconsistent disclosure or definition from the present specification, the disclosure or definition herein shall be deemed to prevail.
  • alternative background noise estimation techniques may be used, such as a noise power estimation technique based on minimum statistics.
  • the background noise level calculated at step 206 may be smoothed and averaged in time or frequency.
  • the output of the background noise estimation at step 206 may be the magnitude/power of the estimated noise for each subband.
  • the system performs a speech intelligibility measurement.
  • the speech intelligibility measurement outputs a value, represented as I, that is indicative of the intelligibility of the speech content in the input signal.
  • the value may be within the range between zero and one, where a value closer to zero indicates that the speech signal has a relatively low intelligibility and where a value closer to one indicates that the speech signal has a relatively high intelligibility.
  • the system calculates a Speech Intelligibility Index ("SII") at step 208.
  • SII Speech Intelligibility Index
  • the Speech Intelligibility Index may be calculated by the techniques described in the American National Standard, "Methods for the Calculation of the Speech Intelligibility Index," ANSI S3.5-1997.
  • other objective intelligibility measures such as the speech articulation index (“AI”) or speech-transmission index (“STI”) can also be used to predict speech intelligibility.
  • AI speech articulation index
  • STI speech-transmission index
  • the speech intelligibility measurement at step 208 may receive the subband average signal power X n, k and subband background noise power B n,k as inputs. Additionally, the speech intelligibility measurement at step 208 may receive or access other data used to generate the speech intelligibility measurement. For example, the speech intelligibility measurement at step 208 may access a band importance function. In this example, the system uses the subband average signal power X n,k and subband background noise power B n,k to calculate a signal-to-noise ratio in each subband.
  • Figure 4 illustrates one implementation of a signal power estimate 402 and a background noise estimate 404 of a speech signal. As shown in Figure 4 , the signal-to-noise ratio varies across the frequency range. In some frequency subbands a high signal-to-noise ratio results (such as in signal portion 406), while in other frequency subbands the signal-to-noise ratio is lower or even negative (such as in signal portion 408).
  • the system may calculate the speech intelligibility measurement based on a band importance function.
  • the band importance function illustrates the recognition that certain frequency bands are more important than others for speech intelligibility purposes.
  • Figure 5 illustrates one implementation of a band importance function 502.
  • the portions of the frequency spectrum between 1000 Hertz and 2500 Hertz have a relatively higher importance value than the very low end of the frequency spectrum (e.g., between 160 Hertz and 400 Hertz) or the very high end of the frequency spectrum (e.g., between 5000 Hertz and 8000 Hertz).
  • the speech intelligibility measurement at step 208 may weigh the importance of each subband to calculate an output value based on the relative importance values and the subband SNR.
  • the speech intelligibility index may be based on the product of a band importance function (e.g., the importance weights of Figure 5 ) and a band audibility function (e.g., the signal-to-noise ratio for each subband). If a first subband has a high signal-to-noise ratio and a high importance value, then it will provide a relatively high contribution to the overall intelligibility measurement. Alternatively, if a different subband has the same signal-to-noise ratio as the first subband but with a lower importance value, then this band will provide a lower contribution to the overall intelligibility measurement than the first subband.
  • a band importance function e.g., the importance weights of Figure 5
  • a band audibility function e.g., the signal-to-noise ratio for each subband.
  • the importance values used for each band of the band importance function may be set based on the number of bands used and relative importance of each frequency range, as described in the American National Standard, "Methods for the Calculation of the Speech Intelligibility Index," ANSI S3.5-1997.
  • the output of the speech intelligibility measurement of step 208 may be a single measurement for the entire signal or may be a measurement for each subband of the signal.
  • the system calculates a target spectral shape to be used later in the process as a reference template for equalization adaptation.
  • Speech averaged over a long period of time has a typical subband shape.
  • the overall shape may be influenced if the talker is male or female or if there is noise present.
  • Two example Long-Term Average Speech Shape (“LTASS") subband shapes are shown in Figure 6 .
  • Figure 6 shows a first template 602 that represents a talker in quiet conditions, and a second template 604 that represents a talker in noisy conditions.
  • the actual LTASS shapes may change based on signal conditions and other factors.
  • the system may use the speech intelligibility measurement ( I ) from step 208 to calculate a weighted mix of two predetermined LTASS templates.
  • more than two predetermined LTASS templates may be used to calculate the output template shape.
  • the speech intelligibility measurement is relatively high, then the average speech signal processed by the system is likely to be more similar to the LTASS shape in the quite conditions.
  • the speech intelligibility measurement is relatively low, then the average speech signal processed by the system is likely to be more similar to the LTASS shape in noisy conditions.
  • the weighted long-term speech curve (e.g., the weighted mix of multiple predetermined templates) that is output from step 210 is used as at least part of the target for adaptation of the equalization coefficients.
  • the equalized output during the adaptation process may look relatively similar in magnitude at a subband level to the weighted long-term speech curve template.
  • the ability of the shapes to match is a moving target because the equalization coefficients and the weighted long-term speech curve shape may change based on signal conditions.
  • the weighted long-term speech curve template is used as a reference when modifying speech spectra shape.
  • Standard speech spectrums for different vocal efforts, namely normal, raised, loud and shout can be found in the American National Standard, "Methods for the Calculation of the Speech Intelligibility Index," ANSI S3.5-1997.
  • those templates may be adjusted to match the actual user environments, such as additive noise level, room acoustics, and microphone frequency response.
  • the standard free-field LTASS templates may be adjusted based on the impulse response of the space (e.g., a known impulse response of a vehicle compartment) where the input signal is captured.
  • the standard free-field LTASS templates may be adjusted based on the microphone impulse response of the microphone used to capture the input signal.
  • L 1 and L 2 are the reference LTASS templates for quiet and noisy conditions, respectively
  • I is the speech intelligibility index limited to be in the range between zero and one.
  • w is limited to be in the range between zero and one.
  • the fixed constants (e.g., 0.45 and 0.3) in the weight factor equation are merely examples, and may be adjusted to control the characteristics of the weighted mix of LTASS templates.
  • the constant values may be adjusted to more heavily favor the quiet LTASS template over the noisy LTASS template in the weighting equation.
  • the output of the weighted long-term speech curve adjustment at step 210 is a weighted long-term speech curve, represented as L n,k .
  • the weighted long-term speech curve may be generated based on the first predetermined long-term average speech curve (e.g., the quite conditions template), the second predetermined long-term average speech curve (e.g., the noisy conditions template), and the speech intelligibility measurement.
  • the system may perform a normalization function at step 212.
  • the weighted long-term speech curve template may be scaled based on the current conditions of the input signal and the noise estimate.
  • an overall energy constraint may be enforced so that the average signal power after applying equalization gains would be similar to the original signal power without equalization.
  • This is achieved by calculating a scaling factor ( ⁇ n ) which is applied to the weighted long-term speech curve template output from step 210 before the template is used in the equalization coefficient adaptation process.
  • This normalization serves to minimize the difference between the average input signal power and the average output signal power. For example, the difference in some implementations may be within 1.8 dB.
  • the system may perform adaptive equalization based on the normalized LTASS template to improve speech intelligibility of the input signal.
  • the adaptive equalization process includes error signal generation at step 214, application of the prior equalization coefficients at step 216, equalization coefficient control at step 218, and application of the new adapted equalization coefficients at step 220.
  • the system generates an error signal e n,k .
  • the adaptive equalization system serves to adjust its equalization coefficients in order to minimize the value of the error signal.
  • the error signal is calculated based on the weighted long-term speech curve template L n,k (with or without normalization), the subband background noise power B n,k , and a processed version of the input speech signal.
  • the error signal may be determined without including the subband background noise power B n,k in the calculation.
  • the processed version of the input speech signal used to generate the error signal may be calculated at step 216, where the system applies a prior version of the equalization coefficients ( G n-1,k ) to a power spectrum of the speech signal to generate an equalized signal.
  • This equalized signal is compared to the weighted long-term speech curve template (e.g., the normalized speech curve from step 212) at step 214. Specifically, the system generates a summed signal by summing the background noise level estimate from step 206 with the normalized speech curve from step 212. The difference between the summed signal and the equalized signal from step 216 results in the error signal.
  • the weighted long-term speech curve template e.g., the normalized speech curve from step 212
  • the system updates its equalization coefficients in a feedback loop that attempts to drive the error signal to zero.
  • the updates to the equalization coefficients may be smoothed.
  • ⁇ is the step size
  • ⁇ n is the scaling factor
  • B is the background noise estimation.
  • the value of the step size variable may be set to control the speed of adaptation.
  • the step size may be
  • the system may apply one or more limits on the adaptation of the equalization coefficients.
  • the system may place a signal-to-noise ratio constraint on the adaptation.
  • the system may calculate a signal-to-noise ratio of the speech signal, compare the signal-to-noise ratio to a predetermined upper threshold (e.g., 15 dB) or a predetermined lower threshold (e.g., 6 dB), and limit a boosting gain of the equalization coefficients in response to a determination that the signal-to-noise ratio is above the predetermined upper threshold or below the predetermined lower threshold.
  • a predetermined upper threshold e.g. 15 dB
  • a predetermined lower threshold e.g. 6 dB
  • the system may place an intelligibility constraint on the adaptation of the equalization coefficients.
  • the system may determine whether an adaptation of the equalization coefficients based on the weighted long-term speech curve would increase or decrease the speech intelligibility measurement of the speech signal.
  • the adaptation of the equalization coefficients may be limited in response to a determination that the adaptation of the equalization coefficients would decrease the speech intelligibility measurement. With this constraint, the adaptation of the equalization coefficients should not decrease the intelligibility contribution of each sub-band. If the intelligibility of each subband is not reduced, then the intelligibility of the entire signal should also not be decreased.
  • the system may use step size control to constrain adaptation. For example, adaptation is faster when the average speech is far away from the reference template and slower when close.
  • the system applies the new adapted version of the equalization coefficients ( G n,k ) to the speech signal on a subband basis.
  • the subbands overlap so there is already smoothing over frequency.
  • the equalization coefficients may be smoothed over time and/or frequency at step 218.
  • the signal is resynthesized from the multiple subbands. For example, the signal may be converted back to a pulse code modulation ("PCM") signal.
  • PCM pulse code modulation
  • the output signal from step 222 may have a higher level of intelligibility than the input signal received at step 202.
  • Each of the processes described herein may be encoded in a computer-readable storage medium (e.g., a computer memory), programmed within a device (e.g., one or more circuits or processors), or may be processed by a controller or a computer. If the processes are performed by software, the software may reside in a local or distributed memory resident to or interfaced to a storage device, a communication interface, or non-volatile or volatile memory in communication with a transmitter.
  • the memory may include an ordered listing of executable instructions for implementing logic.
  • Logic or any system element described may be implemented through optic circuitry, digital circuitry, through source code, through analog circuitry, or through an analog source, such as through an electrical, audio, or video signal.
  • the software may be embodied in any computer-readable or signal-bearing medium, for use by, or in connection with an instruction executable system, apparatus, or device.
  • a system may include a computer-based system, a processor-containing system, or another system that may selectively fetch instructions from an instruction executable system, apparatus, or device that may also execute instructions.
  • a “computer-readable storage medium,” “machine-readable medium,” “propagated-signal” medium, and/or “signal-bearing medium” may comprise a medium (e.g., a non-transitory medium) that stores, communicates, propagates, or transports software or data for use by or in connection with an instruction executable system, apparatus, or device.
  • the machine-readable medium may selectively be, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.
  • a non-exhaustive list of examples of a machine-readable medium would include: an electrical connection having one or more wires, a portable magnetic or optical disk, a volatile memory, such as a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or Flash memory), or an optical fiber.
  • a machine-readable medium may also include a tangible medium, as the software may be electronically stored as an image or in another format (e.g., through an optical scan), then compiled, and/or interpreted or otherwise processed. The processed medium may then be stored in a computer and/or machine memory.
EP12166906.3A 2012-05-04 2012-05-04 Adaptives Ausgleichssystem Active EP2660814B1 (de)

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EP12166906.3A EP2660814B1 (de) 2012-05-04 2012-05-04 Adaptives Ausgleichssystem
CA2814434A CA2814434C (en) 2012-05-04 2013-05-01 Adaptive equalization system

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Cited By (4)

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Publication number Priority date Publication date Assignee Title
JP2013254022A (ja) * 2012-06-05 2013-12-19 Nippon Telegr & Teleph Corp <Ntt> 音声明瞭度推定装置、音声明瞭度推定方法及びそのプログラム
CN105989853A (zh) * 2015-02-28 2016-10-05 科大讯飞股份有限公司 一种音频质量评测方法及系统
GB2553571A (en) * 2016-09-12 2018-03-14 Jaguar Land Rover Ltd Apparatus and method for privacy enhancement
IT202000010435A1 (it) * 2020-05-08 2021-11-08 Rai Radiotelevisione Italiana Spa Metodo per migliorare la percezione della qualita' di un segnale audio digitale emesso da un ricevitore di segnali televisivi, in particolare del tipo a schermo piatto, e relativo dispositivo

Citations (3)

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Publication number Priority date Publication date Assignee Title
US20090132248A1 (en) * 2007-11-15 2009-05-21 Rajeev Nongpiur Time-domain receive-side dynamic control
US20090304215A1 (en) * 2002-07-12 2009-12-10 Widex A/S Hearing aid and a method for enhancing speech intelligibility
US7844453B2 (en) 2006-05-12 2010-11-30 Qnx Software Systems Co. Robust noise estimation

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090304215A1 (en) * 2002-07-12 2009-12-10 Widex A/S Hearing aid and a method for enhancing speech intelligibility
US7844453B2 (en) 2006-05-12 2010-11-30 Qnx Software Systems Co. Robust noise estimation
US20090132248A1 (en) * 2007-11-15 2009-05-21 Rajeev Nongpiur Time-domain receive-side dynamic control

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2013254022A (ja) * 2012-06-05 2013-12-19 Nippon Telegr & Teleph Corp <Ntt> 音声明瞭度推定装置、音声明瞭度推定方法及びそのプログラム
CN105989853A (zh) * 2015-02-28 2016-10-05 科大讯飞股份有限公司 一种音频质量评测方法及系统
GB2553571A (en) * 2016-09-12 2018-03-14 Jaguar Land Rover Ltd Apparatus and method for privacy enhancement
GB2553571B (en) * 2016-09-12 2020-03-04 Jaguar Land Rover Ltd Apparatus and method for privacy enhancement
US10629181B2 (en) 2016-09-12 2020-04-21 Jaguar Land Rover Limited Apparatus and method for privacy enhancement
IT202000010435A1 (it) * 2020-05-08 2021-11-08 Rai Radiotelevisione Italiana Spa Metodo per migliorare la percezione della qualita' di un segnale audio digitale emesso da un ricevitore di segnali televisivi, in particolare del tipo a schermo piatto, e relativo dispositivo

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CA2814434C (en) 2016-08-16
EP2660814B1 (de) 2016-02-03
CA2814434A1 (en) 2013-11-04

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