Abstract:
A speech encoder comprises an encoding element for encoding a noise reduced speech signal, and a noise suppression element that takes a noisy speech signal and generates the noise reduced speech signal by maximizing the signal to noise ratio (SNR) of the noisy speech signal without suppressing the voiced speech components of the noisy speech signal. The noise suppression element may use harmonic modeling techniques that maximize the SNR in each sub-band of the noisy speech signal by reconstructing the voiced speech components of the noisy voiced speech signal emphasizing harmonic frequencies within each sub-band. The SNR is further maximized by eliminating noise components between signal peaks at the harmonic frequencies, and eliminating noise at signal peaks at the harmonic frequencies by smoothing harmonic parameters generated by the reconstruction of the voiced speech components of the noisy speech signal.

Description:
FIELD OF THE INVENTION 
   The present invention relates generally to speech coding systems, and more particularly, to a method and apparatus for improved noise reduction in a speech encoder. 
   BACKGROUND OF THE INVENTION 
   In speech coding systems, reducing background noise in speech signals to improve the quality of processed speech is a primary endeavor. This fact is particularly true for lower signal to background noise ratios A typical speech coding system comprises an encoder, a transmission channel, and a decoder. Parameters for synthesizing speech signals are transmitted from the encoder over the transmission channel to the decoder. The decoder then uses the parameters to synthesize the desired speech signal. 
   In wireless communications systems, the most common form of speech coders use linear predictive methods. One example linear predictive method is Code Excited Linear Prediction (CELP). A general diagram of a CELP encoder  100  is shown in  FIG. 1A. A  CELP encoder uses a model of the human vocal tract in order to reproduce a speech input signal. The parameters for the model are actually extracted from the speech signal being reproduced, and it is these parameters that are sent to a decoder  112 , which is illustrated in FIG.  1 B. Decoder  112  uses the parameters in order to reproduce the speech signal. Referring to  FIG. 1A , synthesis filter  104  is a linear predictive filter and serves as the vocal tract model for CELP encoder  100 . Synthesis filter  104  takes an input excitation signal μ(n) and synthesizes an estimate of speech input s(n) by modeling the correlations introduced into speech by the vocal tract and applying them to the excitation signal μ(n). 
   In CELP encoder  100  speech is broken up into frames, usually 20 ms each, and parameters for synthesis filter  104  are determined for each frame. Once the parameters are determined, an excitation signal μ(n) is chosen for that frame. The excitation signal is then synthesized, producing a synthesized speech signal s′(n). The synthesized frame s′(n) is then compared to the actual speech input frame s(n) and a difference or error signal e(n) is generated by subtractor  106 . The subtraction function is typically accomplished via an adder or similar functional component as those skilled in the art will be aware. Actually, excitation signal μ(n) is generated from a predetermined set of possible signals by excitation generator  102 . In CELP encoder  100 , all possible signals in the predetermined set are tried in order to find the one that produces the smallest error signal e(n). Once this particular excitation signal μ(n) is found, the signal and the corresponding filter parameters are sent to decoder  112  (FIG.  1 B), which reproduces the synthesized speech signal s′(n). Signal s′(n) is reproduced in decoder  112  by using an excitation signal μ(n), as generated by decoder excitation generator  114 , and synthesizing it using decoder synthesis filter  116 . 
   By choosing the excitation signal that produces the smallest error signal e(n), a very good approximation of speech input s(n) can be reproduced in decoder  112 . The spectrum of error signal e(n), however, will be very flat, as illustrated by curve  204  in FIG.  2 . The flatness can create problems in that the signal-to-noise ratio (SNR), with regard to synthesized speech signal s′(n) (curve  202 ), may become too small for effective reproduction of speech signal s(n). This problem is especially prevalent in the higher frequencies where, as illustrated in  FIG. 2 , there is typically less energy in the spectrum of s′(n). In order to combat this problem, CELP encoder  100  includes a feedback path that incorporates error weighting filter  108 . The function of error weighting filter  108  is to shape the spectrum of error signal e(n) so that the noise spectrum is concentrated in areas of high voice content. In effect, the shape of the noise spectrum associated with the weighted error signal e w (n) tracks the spectrum of the synthesized speech signal s′(n), as illustrated in  FIG. 2  by curve  206 . In this manner, the SNR is improved and the perceptual quality of the reproduced speech is increased. 
   If, however, speech input s(n) is noisy, then some type of noise reduction must be performed on speech input s(n) to maintain an adequate quality of voice reproduction in decoder  112 . Traditional noise suppressors can reduce the background noise significantly, but they also distort the speech signal significantly due to the significant modification of the spectral envelope. As a result, the perceptual naturalness of the voiced speech signal is reduced sometimes significantly. Therefore, the requirement for noise suppression and the requirement for perceptually natural voiced signals make it difficult to effectively achieve both simultaneously. 
   SUMMARY OF THE INVENTION 
   There is provided a speech encoder, comprising an encoding element for encoding a noise reduced speech signal, and a noise suppression element that takes a noisy speech signal and generates the noise reduced speech signal by maximizing the signal to noise ratio (SNR) of the noisy speech signal without significantly suppressing the speech components of the noisy speech signal. In one particular embodiment, the noise suppression element uses harmonic modeling techniques that maximizes the SNR in each sub-band of the noisy speech signal by reconstructing the noisy speech signal emphasizing harmonic frequencies within each sub-band. The SNR is further maximized eliminating noise components between harmonic peaks, and eliminating noise at harmonic peaks by smoothing harmonic parameters generated by the reconstruction of the noisy speech. 
   There is also provided, a speech communication system, comprising a speech encoder, which includes an encoding element for encoding a noise reduced speech signal, and a noise suppression element. The speech communication system also includes a decoder that generates a synthesized noise reduced speech signal, which is an estimate of the noise reduced speech signal, from speech parameters generated by the encoding element, and a transmission channel for transmitting the speech parameters from the speech encoder to the decoder. 
   There is also provided a method of noise suppression in a speech encoder, comprising the steps of reconstructing a noisy speech signal emphasizing harmonic frequencies within the noisy speech signals, then eliminating noise components between signal peaks at the harmonic frequencies. Next, the method includes the step of eliminating noise components at the harmonic peaks by smoothing harmonic parameters generated by the reconstructing step, and then generating a noise reduced speech signal. 
   In addition, further embodiments and implementations are discussed in more detail below. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     In the figures of the accompanying drawings, like reference numbers correspond to like elements, in which: 
       FIG. 1A  is a block diagram illustrating an example speech encoder. 
       FIG. 1B  is a block diagram illustrating an example speech decoder that works in conjunction with the encoder illustrated in FIG.  1 A. 
       FIG. 2  is a diagram illustrating the signal to noise ratio for a speech signal versus a noise signal in an encoder such as the encoder illustrated in FIG.  1 A. 
       FIG. 3  is a block diagram illustrating a speech communication system in accordance with one embodiment of the invention. 
       FIG. 4  is a diagram illustrating the signal to noise ratio for a speech signal in the speech communication system illustrated in FIG.  3 . 
       FIG. 5  is a process flow diagram illustrating a method of noise suppression in a speech encoder in accordance with the invention. 
       FIG. 6  is a block diagram illustrating an example wireless communication system. 
       FIG. 7  is a block diagram illustrating one example embodiment of a wireless local loop. 
       FIG. 8  is a block diagram illustrating a second example embodiment of a wireless local loop. 
       FIG. 9  is a block digram illustrating an example cordless phone system. 
       FIG. 10  is a block diagram illustrating an example system for transmitting voice over the Internet. 
   

   DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     FIG. 3  illustrates a speech coding system  300  in accordance with one embodiment of the invention. Speech coding system  300  comprises a noise suppression element  302 , an encoder  304 , a transmission channel  306 , and a decoder  308 . Noise suppression element  302  and encoder  304  form a modified speech encoder  310 . Noise suppression element  302  takes a noisy speech signal ns(n) and produces a noise reduced speech signal ns′(n). The noise reduced speech signal ns′(n) is sent to encoder  304 , which encodes ns′(n) and transmits encoding parameters to decoder  308  over transmission channel  306 . For example, encoder  304  may be a linear predictive encoder, and decoder  308  may be a corresponding linear predictive decoder. In particular, encoder  304  may be a CELP encoder such as that disclosed in co-pending U.S. Application Ser. No. 09/625,088, titled “Method and Apparatus for Improving Weighting Filters in a CELP Encoder,” which is incorporated herein by reference in its entirety. Similarly, an example of a CELP decoder that may be used with the invention is disclosed in co-pending U.S. Application Ser. No. 09/624,187, titled “Method and Apparatus for Using Harmonic Modeling in an Improved Speech Decoder,” which is also incorporated herein by reference in its entirety. 
   While the invention will generally be discussed in relation to CELP encoding, those skilled in the art will recognize that there are many types of linear predictive coding (LPC) techniques. For example, other LPC techniques include QCELP, MELP, and HE-LPC, to name a few. As such, those skilled in the art will recognize that any of these alternative LPC techniques may be used without deviating from the scope of the invention. Therefore, CELP is used solely as an example and is not intended to limit the invention in any way. 
     FIG. 4  illustrates a general approach to noise suppression in a speech coding system. Spectrum  402  represents a spectrum for voiced speech, and noise level  404  represents the level of noise present in spectrum  402 . Typically, for narrow band signal, spectrum  402  will extend from 0 Hz to 4 KHz. Spectrum  402  is divided into a plurality of sub-bands  406 . The number of sub-bands  406  is variable, however, a typical embodiment will employ 20 sub-bands  406 . Once the spectrum is divided into sub-bands  406 , the SNR for each sub-band  406  is estimated. As can be seen in  FIG. 4 , sub-bands  406  do not need to be of equal width. In fact, for sub-bands  406  at higher frequencies, it is better to use wider bands  406   
   After estimating the SNR for each band  406 , an attempt is made to improve the over-all SNR by reducing the energy of the noisy channels (sub-bands  406 ). The gain reduction factor is based on the SNR value of the current channel. Unfortunately, common techniques for noise suppression will distort and suppress speech spectrum  402  as well. This distortion degrades the perceptual naturalness of the voiced speech. In other words, there is some conflict between the noise level reduction and the naturalness. Therefore, while this approach is efficient for unvoiced signals, it is not sufficient for use when spectrum  402  represents a voiced speech signal. In one embodiment, noise suppression element  302  uses the SNR estimating technique when ns(n) is a non-voiced speech signal. But noise suppression element  302  detects when ns(n) represents a voiced speech signal, and uses or combines an alternative method to suppress the noise that does not distort the voice speech spectrum  402  of ns(n). For example, the spectrum can be divided into the harmonic structure area where the new noise suppression technique is used and the non-harmonic area where the traditional noise suppression technique is employed. 
   The basic alternative method is illustrated in FIG.  5 . First, in step  502 , noise suppression element  302  finds the harmonic peaks  408  in each sub-band  406  of spectrum  402 . For example, in  FIG. 4  there are four peaks  408   a,    408   b,    408   c,  and  408   d  in the first three sub-bands  406 . Magnitude and phase specify a harmonic peak and there will be a plurality of harmonics within each sub-band  406 . Then, in step  504 , the harmonic parameters associated with the synthesized periodic signal are smoothed. In step  506 , the harmonics  408  are interpolated. 
   The above steps  502 - 506  represent a process referred to as harmonic modeling. In one sample embodiment, the harmonic modeling is performed using Prototype Waveform Interpolation (PWI). In general, the perceptual importance of the periodicity in voiced speech led to the development of waveform interpolation techniques. PWI exploits the fact that pitch-cycle waveforms in a voiced segment evolve slowly with time. As a result, it is not necessary to know every pitch-cycle to recreate a highly accurate waveform. The pitch-cycle waveforms that are not known are then derived by means of interpolation. The pitch-cycles that are known are referred to as the Prototype Waveforms. 
   PWI works extremely well for voiced segments, however, it is not applicable to unvoiced speech. Therefore, in step  508 , the noise present in unvoiced frequency domain must be suppressed using the method of estimating SNR described above. Noise suppression at points  410   a  and  410   b  can be accomplished using PWI only or combining PWI with the method of estimating SNR described above. In so doing, WI represents speech with a series of evolving waveforms. For voiced speech, these waveforms are simply pitch-cycles. For unvoiced speech and background noise, the waveforms are of varying lengths and contain mostly noise-like signals. 
   In step  510 , the synthesized periodic signals are combined within each sub-band  406 . Then in step  512 , a noise suppressed speech signal is generated from the synthesized periodic signals in each band  406 . Therefore, noise suppression element  302  smoothes out spectrum  402 , making it less noisy across all bands  406 , which greatly improves the SNR for spectrum  402  across all bands  406 . 
   In step  514 , the noise suppressed speech signal is encoded, using CELP for example. In step  516 , encoding parameters related to the noise suppressed speech signal are transmitted to a decoder, where, in step  508 , they are decoded. Decoding of the parameters allows for synthesis of a noise reduced speech signal in the decoder. 
   Those skilled in the art will recognize that speech coding system  300  may be incorporated in a variety of voice communication systems. For example, speech coding system  300  is easily included in wireless communications systems, such as a cellular or PCS systems, regardless of the air interface or communications protocol used by the wireless communications system. In this case, transmission channel  306  is an RF transmission channel. Other embodiments that incorporate speech coding system  300  and a RF transmission channel  306  are cordless telephone systems and wireless local loops. 
   The architecture of one implementation of a cellular network  600  is depicted in block form in FIG.  6 . The network  600  is divided into four interconnected components or subsystems: A Mobile Station (MS)  602 , a Base Station Subsystem (BSS)  610 , and a Network Switching Subsystem (NSS)  618 . Generally, a MS  602  is the mobile equipment or phone carried by the user. And a BSS  610  interfaces with multiple MS&#39;s  602  to manage the radio transmission paths between the MS&#39;s  602  and NSS  618 . In turn, the NSS  618  manages system-switching functions and facilitates communications with other network such as the PSTN and the ISDN. 
   MS&#39;s  602  communicate with the BSS  610  across a standardized radio air interface  604 . BSS  610  is comprised of multiple base transceiver stations (BTS)  608  and base station controllers (BSC)  612 . BTS  608  is usually in the center of a cell and consists of one or more radio transceivers with an antenna. It establishes radio links and handles radio communications over the air interface with MS  602  within the cell. The transmitting power of the transceiver defines the size of the cell. Each BSC  612  manages BTS&#39;s  608 . The total number of transceivers per a particular controller could be in the hundreds. The transceiver-controller communication is over a standardized “Abis” interface  606 . BSC  612  allocates and manages radio channels and controls handovers of calls between its transceivers. 
   BSC  612 , in turn, communicates with NSS  618  over a standardized interface  614 . A Mobile Switching Center (MSC)  620  is the primary component of the NSS  618 . MSC  620  manages communications between MS&#39;s  602  and between MS&#39;s  602  and public networks  630 . Examples of public networks  630  that the mobile switching center may interface with include Integrated Services Digital Network (ISDN)  632 , Public Switched Telephone Network (PSTN)  634 , Public Land Mobile Network (PLMN)  636  and Packet Switched Public Data Network (PSPDN)  638 . 
   Cellular networks, like the example depicted in  FIG. 6 , provide mobile communications ability for wide areas of coverage. The networks essentially replace the traditional wired networks for users in large areas. But wireless technology can also be used to replace smaller portions of the traditional wired network. 
   Each home or office in the industrialized world is equipped with at least one phone line. Each line represents a connection to the larger telecommunications network. This final connection is termed the local loop and expenditures on this portion of the telephone network account for nearly half of total expenditures. Wireless technology can greatly reduce the cost of installing this portion of the network in remote rural areas historically lacking telephone service, in existing networks striving to keep up with demand, and in emerging economies trying to develop their telecommunications infrastructure. 
     FIG. 7  illustrates the architecture of one implementation of a wireless local loop (WLL). It consists of a cluster of Portable Handsets (PHS)  710 , and a base station  720  equipped with an antenna  722 . Traditionally, the handsets would be fixed landlines connected to the network via a twisted pair of copper. Recent developments have allowed the use of more advanced technology such as fiber optic. The advanced technology results in higher quality voice transmission and is more suited to the integration of voice and data in telecommunications. But all of these technologies require the installation of cables or wires that are costly to install and once installed are not easily repositioned. 
   Fortunately, the wired connection can be replaced as shown in FIG.  7 . In  FIG. 7 , a network  730  is connected to a centrally located base station  720 . The base station could be at the center of an office building, for example. The base station then interfaces with PHS  710  via an air interface  712 . Thus, the costly installation of wires or cables is eliminated and flexible use and expansion of PHS  710  is possible. 
     FIG. 8  illustrates an alternative implementation  800  of WLL. This implementation could be utilized in areas where cellular coverage is good. It consists of handsets (HS)  810  and a base station  820 . In this implementation HS&#39;s  810  are wired to base station  820  and base station  820  interfaces via an antenna  822  over an air interface  832  to a cellular network  830 . In this implementation, the cellular network would be the same as illustrated in  FIG. 6 , with base station  820  taking the place of the mobile handsets in that example. This implementation still requires the installation of costly wiring in the local loop. But it may be suitable for remote areas or areas where access to the network is difficult. 
   Another area in which wireless technology is aiding telecommunications is in the home where the traditional telephone handset is being replaced by the cordless phone system. A cordless phone system  900  implementation is illustrated in  FIG. 9 , and is, in many ways, a mini-version of the WLL systems described above. System  900  consists of a cordless telephone system base station  920  and a cordless handset  910 . Base station  920  communicates with handset  910  over an air interface  924  via an antenna  922  and is connected through a wired connection to the network  930 . Cordless handsets  910  in the home allow for untethered use of handset  910 , enabling the user the freedom to move about as long as they stay in the range of base station  920 . 
   Each of these system implementations have in common the use of radios to communicate voice information over an air interface. Originally, radios used in wireless communications used analog transmission schemes. In recent decades, however, various standards for digital transmission techniques have been developed. The digital standards have greatly increased the quality and capacity of the systems described above, and have allowed for higher quality voice reproduction. 
   In that regard, speech coding system  300  is easily incorporated into the radios of bases  608 ,  720 ,  820 , and  920 , and handsets  602 ,  710 , and  910 , within the systems  600 ,  700 ,  800 , and  900 , described above. Thus, the quality of voice reproduction in systems  600 ,  700 ,  800 , and  900  will be improved even further due to the noise suppression provided by speech coding system  300 . 
   Additionally, voice over Internet is a growing field, seeing wider and wider implementation. A general system  1000  for implementing voice over Internet is illustrated in FIG.  10 . Typically, voice traffic will pass from the Internet  1002  through an Internet Service Provider (ISP)  1004  to an end user. The end user will typically receive the voice traffic via a terminal  1006 , such as a phone or computer. For example, in one embodiment, an Internet telephone call may be initiated by a phone terminal  1010 , which will pass through one ISP  1008 , then through the Internet  1002 , and finally through a second ISP  1004  and to the end user at terminal  1006 . Speech coding system  300  is integrated into a system such as  1000  as easily as it is integrated into a wireless communication system as discussed above. In the case of system  1000 , the noisy speech signal ns(n) and/or the transmission channel  306  may be telephone line signals and channels, respectively. The media used for the transmission channel  306  can, for example, may be fiber optic, coaxial cable, or twisted pair. 
   Those skilled in the art will recognize that there are many systems that utilize speech coding systems to communicate voice speech information. Clearly the invention can be implemented within any such system that must deal with noisy speech signals. Therefore, the above sample systems are by way of example only and are not intended to limit the invention in anyway.