Patent Publication Number: US-8990074-B2

Title: Noise-robust speech coding mode classification

Description:
RELATED APPLICATIONS 
     This application is related to and claims priority from U.S. Provisional Patent Application Ser. No. 61/489,629 filed May 24, 2011, for “Noise-Robust Speech Coding Mode Classification.” 
    
    
     TECHNICAL FIELD 
     The present disclosure relates generally to the field of speech processing. More particularly, the disclosed configurations relate to noise-robust speech coding mode classification. 
     BACKGROUND 
     Transmission of voice by digital techniques has become widespread, particularly in long distance and digital radio telephone applications. This, in turn, has created interest in determining the least amount of information that can be sent over a channel while maintaining the perceived quality of the reconstructed speech. If speech is transmitted by simply sampling and digitizing, a data rate on the order of 64 kilobits per second (kbps) is required to achieve a speech quality of conventional analog telephone. However, through the use of speech analysis, followed by the appropriate coding, transmission, and re-synthesis at the receiver, a significant reduction in the data rate can be achieved. The more accurately speech analysis can be performed, the more appropriately the data can be encoded, thus reducing the data rate. 
     Devices that employ techniques to compress speech by extracting parameters that relate to a model of human speech generation are called speech coders. A speech coder divides the incoming speech signal into blocks of time, or analysis frames. Speech coders typically comprise an encoder and a decoder, or a codec. The encoder analyzes the incoming speech frame to extract certain relevant parameters, and then quantizes the parameters into binary representation, i.e., to a set of bits or a binary data packet. The data packets are transmitted over the communication channel to a receiver and a decoder. The decoder processes the data packets, de-quantizes them to produce the parameters, and then re-synthesizes the speech frames using the de-quantized parameters. 
     Modern speech coders may use a multi-mode coding approach that classifies input frames into different types, according to various features of the input speech. Multi-mode variable bit rate encoders use speech classification to accurately capture and encode a high percentage of speech segments using a minimal number of bits per frame. More accurate speech classification produces a lower average encoded bit rate, and higher quality decoded speech. Previously, speech classification techniques considered a minimal number of parameters for isolated frames of speech only, producing few and inaccurate speech mode classifications. Thus, there is a need for a high performance speech classifier to correctly classify numerous modes of speech under varying environmental conditions in order to enable maximum performance of multi-mode variable bit rate encoding techniques. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram illustrating a system for wireless communication; 
         FIG. 2A  is a block diagram illustrating a classifier system that may use noise-robust speech coding mode classification; 
         FIG. 2B  is a block diagram illustrating another classifier system that may use noise-robust speech coding mode classification; 
         FIG. 3  is a flow chart illustrating a method of noise-robust speech classification; 
         FIGS. 4A-4C  illustrate configurations of the mode decision making process for noise-robust speech classification; 
         FIG. 5  is a flow diagram illustrating a method for adjusting thresholds for classifying speech; 
         FIG. 6  is a block diagram illustrating a speech classifier for noise-robust speech classification; 
         FIG. 7  is a timeline graph illustrating one configuration of a received speech signal with associated parameter values and speech mode classifications; and 
         FIG. 8  illustrates certain components that may be included within an electronic device/wireless device. 
     
    
    
     DETAILED DESCRIPTION 
     The function of a speech coder is to compress the digitized speech signal into a low-bit-rate signal by removing all of the natural redundancies inherent in speech. The digital compression is achieved by representing the input speech frame with a set of parameters and employing quantization to represent the parameters with a set of bits. If the input speech frame has a number of bits Ni and the data packet produced by the speech coder has a number of bits No, the compression factor achieved by the speech coder is Cr=Ni/No. The challenge is to retain high voice quality of the decoded speech while achieving the target compression factor. The performance of a speech coder depends on (1) how well the speech model, or the combination of the analysis and synthesis process described above, performs, and (2) how well the parameter quantization process is performed at the target bit rate of No bits per frame. The goal of the speech model is thus to capture the essence of the speech signal, or the target voice quality, with a small set of parameters for each frame. 
     Speech coders may be implemented as time-domain coders, which attempt to capture the time-domain speech waveform by employing high time-resolution processing to encode small segments of speech (typically 5 millisecond (ms) sub-frames) at a time. For each sub-frame, a high-precision representative from a codebook space is found by means of various search algorithms. Alternatively, speech coders may be implemented as frequency-domain coders, which attempt to capture the short-term speech spectrum of the input speech frame with a set of parameters (analysis) and employ a corresponding synthesis process to recreate the speech waveform from the spectral parameters. The parameter quantizer preserves the parameters by representing them with stored representations of code vectors in accordance with quantization techniques described in A. Gersho &amp; R. M. Gray, Vector Quantization and Signal Compression (1992). 
     One possible time-domain speech coder is the Code Excited Linear Predictive (CELP) coder described in L. B. Rabiner &amp; R. W. Schafer, Digital Processing of Speech Signals 396-453 (1978), which is fully incorporated herein by reference. In a CELP coder, the short term correlations, or redundancies, in the speech signal are removed by a linear prediction (LP) analysis, which finds the coefficients of a short-term formant filter. Applying the short-term prediction filter to the incoming speech frame generates an LP residue signal, which is further modeled and quantized with long-term prediction filter parameters and a subsequent stochastic codebook. Thus, CELP coding divides the task of encoding the time-domain speech waveform into the separate tasks of encoding of the LP short-term filter coefficients and encoding the LP residue. Time-domain coding can be performed at a fixed rate (i.e., using the same number of bits, N 0 , for each frame) or at a variable rate (in which different bit rates are used for different types of frame contents). Variable-rate coders attempt to use only the amount of bits needed to encode the codec parameters to a level adequate to obtain a target quality. One possible variable rate CELP coder is described in U.S. Pat. No. 5,414,796, which is assigned to the assignee of the presently disclosed configurations and fully incorporated herein by reference. 
     Time-domain coders such as the CELP coder typically rely upon a high number of bits, N 0 , per frame to preserve the accuracy of the time-domain speech waveform. Such coders typically deliver excellent voice quality provided the number of bits, N 0 , per frame is relatively large (e.g., 8 kbps or above). However, at low bit rates (4 kbps and below), time-domain coders fail to retain high quality and robust performance due to the limited number of available bits. At low bit rates, the limited codebook space clips the waveform-matching capability of conventional time-domain coders, which are so successfully deployed in higher-rate commercial applications. 
     Typically, CELP schemes employ a short term prediction (STP) filter and a long term prediction (LTP) filter. An Analysis by Synthesis (AbS) approach is employed at an encoder to find the LTP delays and gains, as well as the best stochastic codebook gains and indices. Current state-of-the-art CELP coders such as the Enhanced Variable Rate Coder (EVRC) can achieve good quality synthesized speech at a data rate of approximately 8 kilobits per second. 
     Furthermore, unvoiced speech does not exhibit periodicity. The bandwidth consumed encoding the LTP filter in the conventional CELP schemes is not as efficiently utilized for unvoiced speech as for voiced speech, where periodicity of speech is strong and LTP filtering is meaningful. Therefore, a more efficient (i.e., lower bit rate) coding scheme is desirable for unvoiced speech. Accurate speech classification is necessary for selecting the most efficient coding schemes, and achieving the lowest data rate. 
     For coding at lower bit rates, various methods of spectral, or frequency-domain, coding of speech have been developed, in which the speech signal is analyzed as a time-varying evolution of spectra. See, e.g., R. J. McAulay &amp; T. F. Quatieri, Sinusoidal Coding, in Speech Coding and Synthesis ch. 4 (W. B. Kleijn &amp; K. K. Paliwal eds., 1995). In spectral coders, the objective is to model, or predict, the short-term speech spectrum of each input frame of speech with a set of spectral parameters, rather than to precisely mimic the time-varying speech waveform. The spectral parameters are then encoded and an output frame of speech is created with the decoded parameters. The resulting synthesized speech does not match the original input speech waveform, but offers similar perceived quality. Examples of frequency-domain coders include multiband excitation coders (MBEs), sinusoidal transform coders (STCs), and harmonic coders (HCs). Such frequency-domain coders offer a high-quality parametric model having a compact set of parameters that can be accurately quantized with the low number of bits available at low bit rates. 
     Nevertheless, low-bit-rate coding imposes the critical constraint of a limited coding resolution, or a limited codebook space, which limits the effectiveness of a single coding mechanism, rendering the coder unable to represent various types of speech segments under various background conditions with equal accuracy. For example, conventional low-bit-rate, frequency-domain coders do not transmit phase information for speech frames. Instead, the phase information is reconstructed by using a random, artificially generated, initial phase value and linear interpolation techniques. See, e.g., H. Yang et al., Quadratic Phase Interpolation for Voiced Speech Synthesis in the MBE Model, in 29 Electronic Letters 856-57 (May 1993). Because the phase information is artificially generated, even if the amplitudes of the sinusoids are perfectly preserved by the quantization-de-quantization process, the output speech produced by the frequency-domain coder will not be aligned with the original input speech (i.e., the major pulses will not be in sync). It has therefore proven difficult to adopt any closed-loop performance measure, such as, e.g., signal-to-noise ratio (SNR) or perceptual SNR, in frequency-domain coders. 
     One effective technique to encode speech efficiently at low bit rate is multi-mode coding. Multi-mode coding techniques have been employed to perform low-rate speech coding in conjunction with an open-loop mode decision process. One such multi-mode coding technique is described in Amitava Das et al., Multi-mode and Variable-Rate Coding of Speech, in Speech Coding and Synthesis ch. 7 (W. B. Kleijn &amp; K. K. Paliwal eds., 1995). Conventional multi-mode coders apply different modes, or encoding-decoding algorithms, to different types of input speech frames. Each mode, or encoding-decoding process, is customized to represent a certain type of speech segment, such as, e.g., voiced speech, unvoiced speech, or background noise (non-speech) in the most efficient manner. The success of such multi-mode coding techniques is highly dependent on correct mode decisions, or speech classifications. An external, open loop mode decision mechanism examines the input speech frame and makes a decision regarding which mode to apply to the frame. The open-loop mode decision is typically performed by extracting a number of parameters from the input frame, evaluating the parameters as to certain temporal and spectral characteristics, and basing a mode decision upon the evaluation. The mode decision is thus made without knowing in advance the exact condition of the output speech, i.e., how close the output speech will be to the input speech in terms of voice quality or other performance measures. One possible open-loop mode decision for a speech codec is described in U.S. Pat. No. 5,414,796, which is assigned to the assignee of the present invention and fully incorporated herein by reference. 
     Multi-mode coding can be fixed-rate, using the same number of bits N 0  for each frame, or variable-rate, in which different bit rates are used for different modes. The goal in variable-rate coding is to use only the amount of bits needed to encode the codec parameters to a level adequate to obtain the target quality. As a result, the same target voice quality as that of a fixed-rate, higher-rate coder can be obtained at significant lower average-rate using variable-bit-rate (VBR) techniques. One possible variable rate speech coder is described in U.S. Pat. No. 5,414,796. There is presently a surge of research interest and strong commercial need to develop a high-quality speech coder operating at medium to low bit rates (i.e., in the range of 2.4 to 4 kbps and below). The application areas include wireless telephony, satellite communications, Internet telephony, various multimedia and voice-streaming applications, voice mail, and other voice storage systems. The driving forces are the need for high capacity and the demand for robust performance under packet loss situations. Various recent speech coding standardization efforts are another direct driving force propelling research and development of low-rate speech coding algorithms. A low-rate speech coder creates more channels, or users, per allowable application bandwidth. A low-rate speech coder coupled with an additional layer of suitable channel coding can fit the overall bit-budget of coder specifications and deliver a robust performance under channel error conditions. 
     Multi-mode VBR speech coding is therefore an effective mechanism to encode speech at low bit rate. Conventional multi-mode schemes require the design of efficient encoding schemes, or modes, for various segments of speech (e.g., unvoiced, voiced, transition) as well as a mode for background noise, or silence. The overall performance of the speech coder depends on the robustness of the mode classification and how well each mode performs. The average rate of the coder depends on the bit rates of the different modes for unvoiced, voiced, and other segments of speech. In order to achieve the target quality at a low average rate, it is necessary to correctly determine the speech mode under varying conditions. Typically, voiced and unvoiced speech segments are captured at high bit rates, and background noise and silence segments are represented with modes working at a significantly lower rate. Multi-mode variable bit rate encoders require correct speech classification to accurately capture and encode a high percentage of speech segments using a minimal number of bits per frame. More accurate speech classification produces a lower average encoded bit rate, and higher quality decoded speech. 
     In other words, in source-controlled variable rate coding, the performance of this frame classifier determines the average bit rate based on features of the input speech (energy, voicing, spectral tilt, pitch contour, etc.). The performance of the speech classifier may degrade when the input speech is corrupted by noise. This may cause undesirable effects on the quality and bit rate. Accordingly, methods for detecting the presence of noise and suitably adjusting the classification logic may be used to ensure robust operation in real-world use cases. Furthermore, speech classification techniques previously considered a minimal number of parameters for isolated frames of speech only, producing few and inaccurate speech mode classifications. Thus, there is a need for a high performance speech classifier to correctly classify numerous modes of speech under varying environmental conditions in order to enable maximum performance of multi-mode variable bit rate encoding techniques. 
     The disclosed configurations provide a method and apparatus for improved speech classification in vocoder applications. Classification parameters may be analyzed to produce speech classifications with relatively high accuracy. A decision making process is used to classify speech on a frame by frame basis. Parameters derived from original input speech may be employed by a state-based decision maker to accurately classify various modes of speech. Each frame of speech may be classified by analyzing past and future frames, as well as the current frame. Modes of speech that can be classified by the disclosed configurations comprise at least transient, transitions to active speech and at the end of words, voiced, unvoiced and silence. 
     In order to ensure robustness in the classification logic, the present systems and methods may use a multi-frame measure of background noise estimate (which is typically provided by standard up-stream speech coding components, such as a voice activity detector) and adjust the classification logic based on this. Alternatively, an SNR may be used by the classification logic if it includes information about more than one frame, e.g., if it is averaged over multiple frames. In other words, any noise estimate that is relatively stable over multiple frames may be used by the classification logic. The adjustment of classification logic may include changing one or more thresholds used to classify speech. Specifically, the energy threshold for classifying a frame as “unvoiced” may be increased (reflecting the high level of “silence” frames), the voicing threshold for classifying a frame as “unvoiced” may be increased (reflecting the corruption of voicing information under noise), the voicing threshold for classifying a frame as “voiced” may be decreased (again, reflecting the corruption of voicing information), or some combination. In the case where no noise is present, no changes may be introduced to the classification logic. In one configuration with high noise (e.g., 20 dB SNR, typically the lowest SNR tested in speech codec standardization), the unvoiced energy threshold may be increased by 10 dB, the unvoiced voicing threshold may be increased by 0.06, and the voiced voicing threshold may be decreased by 0.2. In this configuration, intermediate noise cases can be handled either by interpolating between the “clean” and “noise” settings, based on the input noise measure, or using a hard threshold set for some intermediate noise level. 
       FIG. 1  is a block diagram illustrating a system  100  for wireless communication. In the system  100  a first encoder  110  receives digitized speech samples s(n) and encodes the samples s(n) for transmission on a transmission medium  112 , or communication channel  112 , to a first decoder  114 . The decoder  114  decodes the encoded speech samples and synthesizes an output speech signal sSYNTH(n). For transmission in the opposite direction, a second encoder  116  encodes digitized speech samples s(n), which are transmitted on a communication channel  118 . A second decoder  120  receives and decodes the encoded speech samples, generating a synthesized output speech signal sSYNTH(n). 
     The speech samples, s(n), represent speech signals that have been digitized and quantized in accordance with any of various methods including, e.g., pulse code modulation (PCM), companded Haw, or μ-law. In one configuration, the speech samples, s(n), are organized into frames of input data wherein each frame comprises a predetermined number of digitized speech samples s(n). In one configuration, a sampling rate of 8 kHz is employed, with each 20 ms frame comprising 160 samples. In the configurations described below, the rate of data transmission may be varied on a frame-to-frame basis from 8 kbps (full rate) to 4 kbps (half rate) to 2 kbps (quarter rate) to 1 kbps (eighth rate). Alternatively, other data rates may be used. As used herein, the terms “full rate” or “high rate” generally refer to data rates that are greater than or equal to 8 kbps, and the terms “half rate” or “low rate” generally refer to data rates that are less than or equal to 4 kbps. Varying the data transmission rate is beneficial because lower bit rates may be selectively employed for frames containing relatively less speech information. While specific rates are described herein, any suitable sampling rates, frame sizes, and data transmission rates may be used with the present systems and methods. 
     The first encoder  110  and the second decoder  120  together may comprise a first speech coder, or speech codec. Similarly, the second encoder  116  and the first decoder  114  together comprise a second speech coder. Speech coders may be implemented with a digital signal processor (DSP), an application-specific integrated circuit (ASIC), discrete gate logic, firmware, or any conventional programmable software module and a microprocessor. The software module could reside in RAM memory, flash memory, registers, or any other form of writable storage medium. Alternatively, any conventional processor, controller, or state machine could be substituted for the microprocessor. Possible ASICs designed specifically for speech coding are described in U.S. Pat. Nos. 5,727,123 and 5,784,532 assigned to the assignee of the present invention and fully incorporated herein by reference. 
     As an example, without limitation, a speech coder may reside in a wireless communication device. As used herein, the term “wireless communication device” refers to an electronic device that may be used for voice and/or data communication over a wireless communication system. Examples of wireless communication devices include cellular phones, personal digital assistants (PDAs), handheld devices, wireless modems, laptop computers, personal computers, tablets, etc. A wireless communication device may alternatively be referred to as an access terminal, a mobile terminal, a mobile station, a remote station, a user terminal, a terminal, a subscriber unit, a subscriber station, a mobile device, a wireless device, user equipment (UE) or some other similar terminology. 
       FIG. 2A  is a block diagram illustrating a classifier system  200   a  that may use noise-robust speech coding mode classification. The classifier system  200   a  of  FIG. 2A  may reside in the encoders illustrated in  FIG. 1 . In another configuration, the classifier system  200   a  may stand alone, providing speech classification mode output  246   a  to devices such as the encoders illustrated in  FIG. 1 . 
     In  FIG. 2A , input speech  212   a  is provided to a noise suppresser  202 . Input speech  212   a  may be generated by analog to digital conversion of a voice signal. The noise suppresser  202  filters noise components from the input speech  212   a  producing a noise suppressed output speech signal  214   a . In one configuration, the speech classification apparatus of  FIG. 2A  may use an Enhanced Variable Rate CODEC (EVRC). As shown, this configuration may include a built-in noise suppressor  202  that determines a noise estimate  216   a  and SNR information  218 . 
     The noise estimate  216   a  and output speech signal  214   a  may be input to a speech classifier  210   a . The output speech signal  214   a  of the noise suppresser  202  may also be input to a voice activity detector  204   a , an LPC Analyzer  206   a , and an open loop pitch estimator  208   a . The noise estimate  216   a  may also be fed to the voice activity detector  204   a  with SNR information  218  from the noise suppressor  202 . The noise estimate  216   a  may be used by the speech classifier  210   a  to set periodicity thresholds and to distinguish between clean and noisy speech. 
     One possible way to classify speech is to use the SNR information  218 . However, the speech classifier  210   a  of the present systems and methods may use the noise estimate  216   a  instead of the SNR information  218 . Alternatively, the SNR information  218  may be used if it is relatively stable across multiple frames, e.g., a metric that includes SNR information  218  for multiple frames. The noise estimate  216   a  may be a relatively long term indicator of the noise included in the input speech. The noise estimate  216   a  is hereinafter referred to as ns_est. The output speech signal  214   a  is hereinafter referred to as t_in. If, in one configuration, the noise suppressor  202  is not present, or is turned off, the noise estimate  216   a , ns_est, may be pre-set to a default value. 
     One advantage of using a noise estimate  216   a  instead of SNR information  218  is that the noise estimate may be relatively steady on a frame-by-frame basis. The noise estimate  216   a  is only estimating the background noise level, which tends to be relatively constant for long time periods. In one configuration the noise estimate  216   a  may be used to determine the SNR  218  for a particular frame. In contrast, the SNR  218  may be a frame-by-frame measure that may include relatively large swings depending on instantaneous voice energy, e.g., the SNR may swing by many dB between silence frames and active speech frames. Therefore, if SNR information  218  is used for classification, it may be averaged over more than one frame of input speech  212   a . The relative stability of the noise estimate  216   a  may be useful in distinguishing high-noise situations from simply quiet frames. Even in zero noise, the SNR  218  may still be very low in frames where the speaker is not talking, and so mode decision logic using SNR information  218  may be activated in those frames. The noise estimate  216   a  may be relatively constant unless the ambient noise conditions change, thereby avoiding issue. 
     The voice activity detector  204   a  may output voice activity information  220   a  for the current speech frame to the speech classifier  210   a , i.e., based on the output speech  214   a , the noise estimate  216   a  and the SNR information  218 . The voice activity information output  220   a  indicates if the current speech is active or inactive. In one configuration, the voice activity information output  220   a  may be binary, i.e., active or inactive. In another configuration, the voice activity information output  220   a  may be multi-valued. The voice activity information parameter  220   a  is herein referred to as vad. 
     The LPC analyzer  206   a  outputs LPC reflection coefficients  222   a  for the current output speech to speech classifier  210   a . The LPC analyzer  206   a  may also output other parameters such as LPC coefficients (not shown). The LPC reflection coefficient parameter  222   a  is herein referred to as refl. 
     The open loop pitch estimator  208   a  outputs a Normalized Auto-correlation Coefficient Function (NACF) value  224   a , and NACF around pitch values  226   a , to the speech classifier  210   a . The NACF parameter  224   a  is hereinafter referred to as nacf, and the NACF around pitch parameter  226   a  is hereinafter referred to as nacf_at_pitch. A more periodic speech signal produces a higher value of nacf_at_pitch  226   a . A higher value of nacf_at_pitch  226   a  is more likely to be associated with a stationary voice output speech type. The speech classifier  210   a  maintains an array of nacf_at_pitch values  226   a , which may be computed on a sub-frame basis. In one configuration, two open loop pitch estimates are measured for each frame of output speech  214   a  by measuring two sub-frames per frame. The NACF around pitch (nacf_at_pitch)  226   a  may be computed from the open loop pitch estimate for each sub-frame. In one configuration, a five dimensional array of nacf_at_pitch values  226   a  (i.e. nacf_at_pitch[4]) contains values for two and one-half frames of output speech  214   a . The nacf_at_pitch array is updated for each frame of output speech  214   a . The use of an array for the nacf_at_pitch parameter  226   a  provides the speech classifier  210   a  with the ability to use current, past, and look ahead (future) signal information to make more accurate and noise-robust speech mode decisions. 
     In addition to the information input to the speech classifier  210   a  from external components, the speech classifier  210   a  internally generates derived parameters  282   a  from the output speech  214   a  for use in the speech mode decision making process. 
     In one configuration, the speech classifier  210   a  internally generates a zero crossing rate parameter  228   a , hereinafter referred to as zcr. The zcr parameter  228   a  of the current output speech  214   a  is defined as the number of sign changes in the speech signal per frame of speech. In voiced speech, the zcr value  228   a  is low, while unvoiced speech (or noise) has a high zcr value  228   a  because the signal is very random. The zcr parameter  228   a  is used by the speech classifier  210   a  to classify voiced and unvoiced speech. 
     In one configuration, the speech classifier  210   a  internally generates a current frame energy parameter  230   a , hereinafter referred to as E. E  230   a  may be used by the speech classifier  210   a  to identify transient speech by comparing the energy in the current frame with energy in past and future frames. The parameter vEprev is the previous frame energy derived from E  230   a.    
     In one configuration, the speech classifier  210   a  internally generates a look ahead frame energy parameter  232   a , hereinafter referred to as Enext. Enext  232   a  may contain energy values from a portion of the current frame and a portion of the next frame of output speech. In one configuration, Enext  232   a  represents the energy in the second half of the current frame and the energy in the first half of the next frame of output speech. Enext  232   a  is used by speech classifier  210   a  to identify transitional speech. At the end of speech, the energy of the next frame  232   a  drops dramatically compared to the energy of the current frame  230   a . Speech classifier  210   a  can compare the energy of the current frame  230   a  and the energy of the next frame  232   a  to identify end of speech and beginning of speech conditions, or up transient and down transient speech modes. 
     In one configuration, the speech classifier  210   a  internally generates a band energy ratio parameter  234   a , defined as log 2(EL/EH), where EL is the low band current frame energy from 0 to 2 kHz, and EH is the high band current frame energy from 2 kHz to 4 kHz. The band energy ratio parameter  234   a  is hereinafter referred to as bER. The bER  234   a  parameter allows the speech classifier  210   a  to identify voiced speech and unvoiced speech modes, as in general, voiced speech concentrates energy in the low band, while noisy unvoiced speech concentrates energy in the high band. 
     In one configuration, the speech classifier  210   a  internally generates a three-frame average voiced energy parameter  236   a  from the output speech  214   a , hereinafter referred to as vEay. In other configurations, vEav  236   a  may be averaged over a number of frames other than three. If the current speech mode is active and voiced, vEav  236   a  calculates a running average of the energy in the last three frames of output speech. Averaging the energy in the last three frames of output speech provides the speech classifier  210   a  with more stable statistics on which to base speech mode decisions than single frame energy calculations alone. vEav  236   a  is used by the speech classifier  210   a  to classify end of voice speech, or down transient mode, as the current frame energy  230   a , E, will drop dramatically compared to average voice energy  236   a , vEav, when speech has stopped. vEav  236   a  is updated only if the current frame is voiced, or reset to a fixed value for unvoiced or inactive speech. In one configuration, the fixed reset value is 0.01. 
     In one configuration, the speech classifier  210   a  internally generates a previous three frame average voiced energy parameter  238   a , hereinafter referred to as vEprev. In other configurations, vEprev  238   a  may be averaged over a number of frames other than three. vEprev  238   a  is used by speech classifier  210   a  to identify transitional speech. At the beginning of speech, the energy of the current frame  230   a  rises dramatically compared to the average energy of the previous three voiced frames  238   a . Speech classifier  210  can compare the energy of the current frame  230   a  and the energy previous three frames  238   a  to identify beginning of speech conditions, or up transient and speech modes. Similarly at the end of voiced speech, the energy of the current frame  230   a  drops off dramatically. Thus, vEprev  238   a  may also be used to classify transition at end of speech. 
     In one configuration, the speech classifier  210   a  internally generates a current frame energy to previous three-frame average voiced energy ratio parameter  240   a , defined as 10*log 10(E/vEprev). In other configurations, vEprev  238   a  may be averaged over a number of frames other than three. The current energy to previous three-frame average voiced energy ratio parameter  240   a  is hereinafter referred to as vER. vER  240   a  is used by the speech classifier  210   a  to classify start of voiced speech and end of voiced speech, or up transient mode and down transient mode, as vER  240   a  is large when speech has started again and is small at the end of voiced speech. The vER  240   a  parameter may be used in conjunction with the vEprev  238   a  parameter in classifying transient speech. 
     In one configuration, the speech classifier  210   a  internally generates a current frame energy to three-frame average voiced energy parameter  242   a , defined as MIN(20,10*log 10(E/vEav)). The current frame energy to three-frame average voiced energy  242   a  is hereinafter referred to as vER 2 . vER 2   242   a  is used by the speech classifier  210   a  to classify transient voice modes at the end of voiced speech. 
     In one configuration, the speech classifier  210   a  internally generates a maximum sub-frame energy index parameter  244   a . The speech classifier  210   a  evenly divides the current frame of output speech  214   a  into sub-frames, and computes the Root Means Squared (RMS) energy value of each sub-frame. In one configuration, the current frame is divided into ten sub-frames. The maximum sub-frame energy index parameter is the index to the sub-frame that has the largest RMS energy value in the current frame, or in the second half of the current frame. The max sub-frame energy index parameter  244   a  is hereinafter referred to as maxsfe_idx. Dividing the current frame into sub-frames provides the speech classifier  210   a  with information about locations of peak energy, including the location of the largest peak energy, within a frame. More resolution is achieved by dividing a frame into more sub-frames. The maxsfe_idx parameter  244   a  is used in conjunction with other parameters by the speech classifier  210   a  to classify transient speech modes, as the energies of unvoiced or silence speech modes are generally stable, while energy picks up or tapers off in a transient speech mode. 
     The speech classifier  210   a  may use parameters input directly from encoding components, and parameters generated internally, to more accurately and robustly classify modes of speech than previously possible. The speech classifier  210   a  may apply a decision making process to the directly input and internally generated parameters to produce improved speech classification results. The decision making process is described in detail below with references to  FIGS. 4A-4C  and Tables 4-6. 
     In one configuration, the speech modes output by speech classifier  210  comprise: Transient, Up-Transient, Down-Transient, Voiced, Unvoiced, and Silence modes. Transient mode is a voiced but less periodic speech, optimally encoded with full rate CELP. Up-Transient mode is the first voiced frame in active speech, optimally encoded with full rate CELP. Down-transient mode is low energy voiced speech typically at the end of a word, optimally encoded with half rate CELP. Voiced mode is a highly periodic voiced speech, comprising mainly vowels. Voiced mode speech may be encoded at full rate, half rate, quarter rate, or eighth rate. The data rate for encoding voiced mode speech is selected to meet Average Data Rate (ADR) requirements. Unvoiced mode, comprising mainly consonants, is optimally encoded with quarter rate Noise Excited Linear Prediction (NELP). Silence mode is inactive speech, optimally encoded with eighth rate CELP. 
     Suitable parameters and speech modes are not limited to the specific parameters and speech modes of the disclosed configurations. Additional parameters and speech modes can be employed without departing from the scope of the disclosed configurations. 
       FIG. 2B  is a block diagram illustrating another classifier system  200   b  that may use noise-robust speech coding mode classification. The classifier system  200   b  of  FIG. 2B  may reside in the encoders illustrated in  FIG. 1 . In another configuration, the classifier system  200   b  may stand alone, providing speech classification mode output to devices such as the encoders illustrated in  FIG. 1 . The classifier system  200   b  illustrated in  FIG. 2B  may include elements that correspond to the classifier system  200   a  illustrated in  FIG. 2A . Specifically, the LPC analyzer  206   b , open loop pitch estimator  208   b  and speech classifier  210   b  illustrated in  FIG. 2B  may correspond to and include similar functionality as the LPC analyzer  206   a , open loop pitch estimator  208   a  and speech classifier  210   a  illustrated in  FIG. 2A , respectively. Similarly, the speech classifier  210   b  inputs in  FIG. 2B  (voice activity information  220   b , reflection coefficients  222   b , NACF  224   b  and NACF around pitch  226   b ) may correspond to the speech classifier  210   a  inputs (voice activity information  220   a , reflection coefficients  222   a , NACF  224   a  and NACF around pitch  226   a ) in  FIG. 2A , respectively. Similarly, the derived parameters  282   b  in  FIG. 2B  (zcr  228   b , E  230   b , Enext  232   b , bER  234   b , vEav  236   b , vEprev  238   b , vER  240   b , vER 2   242   b  and maxsfe_idx  244   b ) may correspond to the derived parameters  282   a  in  FIG. 2A  (zcr  228   a , E  230   a , Enext  232   a , bER  234   a , vEav  236   a , vEprev  238   a , vER  240   a , vER 2   242   a  and maxsfe_idx  244   a ), respectively. 
     In  FIG. 2B , there is no included noise suppressor. In one configuration, the speech classification apparatus of  FIG. 2B  may use an Enhanced Voice Services (EVS) CODEC. The apparatus of  FIG. 2B  may receive the input speech frames  212   b  from a noise suppressing component external to the speech codec. Alternatively, there may be no noise suppression performed. Since there is no included noise suppressor  202 , the noise estimate, ns_est,  216   b  may be determined by the voice activity detector  204   a . While  FIGS. 2A-2B  describe two configurations where the noise estimate  216   b  is determined by a noise suppressor  202  and a voice activity detector  204   b , respectively, the noise estimate  216   a - b  may be determined by any suitable module, e.g., a generic noise estimator (not shown). 
       FIG. 3  is a flow chart illustrating a method  300  of noise-robust speech classification. In step  302 , classification parameters input from external components are processed for each frame of noise suppressed output speech. In one configuration, (e.g., the classifier system  200   a  illustrated in  FIG. 2A ), classification parameters input from external components comprise ns_est  216   a  and  t _in  214   a  input from a noise suppresser component  202 , nacf  224   a  and nacf_at_pitch  226   a  parameters input from an open loop pitch estimator component  208   a , vad  220   a  input from a voice activity detector component  204   a , and refl  222   a  input from an LPC analysis component  206   a . Alternatively, ns_est  216   b  may be input from a different module, e.g., a voice activity detector  204   b  as illustrated in  FIG. 2B . The t_in  214   a - b  input may be the output speech frames  214   a  from a noise suppressor  202  as in  FIG. 2A  or input frames as  212   b  in  FIG. 2B . Control flow proceeds to step  304 . 
     In step  304 , additional internally generated derived parameters  282   a - b  are computed from classification parameters input from external components. In one configuration, zcr  228   a - b , E  230   a - b , Enext  232   a - b , bER  234   a - b , vEav  236   a - b , vEprev  238   a - b , vER  240   a - b , vER 2   242   a - b  and maxsfe_idx  244   a - b  are computed from t_in  214   a - b . When internally generated parameters have been computed for each output speech frame, control flow proceeds to step  306 . 
     In step  306 , NACF thresholds are determined, and a parameter analyzer is selected according to the environment of the speech signal. In one configuration, the NACF threshold is determined by comparing the ns_est parameter  216   a - b  input in step  302  to a noise estimate threshold value. The ns_est information  216   a - b  may provide an adaptive control of a periodicity decision threshold. In this manner, different periodicity thresholds are applied in the classification process for speech signals with different levels of noise components. This may produce a relatively accurate speech classification decision when the most appropriate NACF, or periodicity, threshold for the noise level of the speech signal is selected for each frame of output speech. Determining the most appropriate periodicity threshold for a speech signal allows the selection of the best parameter analyzer for the speech signal. Alternatively, SNR information  218  may be used to determine the NACF threshold, if the SNR information  218  includes information about multiple frames and is relatively stable from frame to frame. 
     Clean and noisy speech signals inherently differ in periodicity. When noise is present, speech corruption is present. When speech corruption is present, the measure of the periodicity, or nacf  224   a - b , is lower than that of clean speech. Thus, the NACF threshold is lowered to compensate for a noisy signal environment or raised for a clean signal environment. The speech classification technique of the disclosed systems and methods may adjust periodicity (i.e., NACF) thresholds for different environments, producing a relatively accurate and robust mode decision regardless of noise levels. 
     In one configuration, if the value of ns_est  216   a - b  is less than or equal to a noise estimate threshold, NACF thresholds for clean speech are applied. Possible NACF thresholds for clean speech may be defined by the following table: 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Threshold for Type 
                 Threshold Name 
                 Threshold Value 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 Voiced 
                 VOICEDTH 
                 .605 
               
               
                   
                 Transitional 
                 LOWVOICEDTH 
                 .5 
               
               
                   
                 Unvoiced 
                 UNVOICEDTH 
                 .35 
               
               
                   
                   
               
            
           
         
       
     
     However, depending on the value of ns_est  216   a - b , various thresholds may be adjusted. For example, if the value of ns_est  216   a - b  is greater than a noise estimate threshold, NACF thresholds for noisy speech may be applied. The noise estimate threshold may be any suitable value, e.g., 20 dB, 25 dB, etc. In one configuration, the noise estimate threshold is set to be above what is observed under clean speech and below what is observed in very noisy speech. Possible NACF thresholds for noisy speech may be defined by the following table: 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Threshold for Type 
                 Threshold Name 
                 Threshold Value 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 Voiced 
                 VOICEDTH 
                 .585 
               
               
                   
                 Transitional 
                 LOWVOICEDTH 
                 .5 
               
               
                   
                 Unvoiced 
                 UNVOICEDTH 
                 .35 
               
               
                   
                   
               
            
           
         
       
     
     In the case where no noise is present (i.e., ns_est  216   a - b  does not exceed the noise estimate threshold), the voicing thresholds may not be adjusted. However, the voicing NACF threshold for classifying a frame as “voiced” may be decreased (reflecting the corruption of voicing information) when there is high noise in the input speech. In other words, the voicing threshold for classifying “voiced” speech may be decreased by 0.2, as seen in Table 2 when compared to Table 1. 
     Alternatively, or in addition to, modifying the NACF thresholds for classifying “voiced” frames, the speech classifier  210   a - b  may adjust one or more thresholds for classifying “unvoiced” frames based on the value of ns_est  216   a - b . There may be two types of NACF thresholds for classifying “unvoiced” frames that are adjusted based on the value of ns_est  216   a - b : a voicing threshold and an energy threshold. Specifically, the voicing NACF threshold for classifying a frame as “unvoiced” may be increased (reflecting the corruption of voicing information under noise). For example, the “unvoiced” voicing NACF threshold may increase by 0.06 in the presence of high noise (i.e., when ns_est  216   a - b  exceeds the noise estimate threshold), thereby making the classifier more permissive in classifying frames as “unvoiced.” If multi-frame SNR information  218  is used instead of ns_est  216   a - b , a low SNR (indicating the presence of high noise), the “unvoiced” voicing threshold may increase by 0.06. Examples of adjusted voicing NACF thresholds may be given according to Table 3: 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE 3 
               
               
                   
                   
               
               
                   
                 Threshold for Type 
                 Threshold Name 
                 Threshold Value 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
            
               
                   
                 Voiced 
                 VOICEDTH 
                 .75 
               
               
                   
                 Transitional 
                 LOWVOICEDTH 
                 .5 
               
               
                   
                 Unvoiced 
                 UNVOICEDTH 
                 .41 
               
               
                   
                   
               
            
           
         
       
     
     The energy threshold for classifying a frame as “unvoiced” may also be increased (reflecting the high level of “silence” frames) in the presence of high noise, i.e., when ns_est  216   a - b  exceeds the noise estimate threshold. For example, the unvoiced energy threshold may increase by 10 dB in high noise frames, e.g., the energy threshold may be increased from −25 dB in the clean speech case to −15 dB in the noisy case. Increasing the voicing threshold and the energy threshold for classifying a frame as “unvoiced” may make it easier (i.e., more permissive) to classify a frame as unvoiced as the noise estimate gets higher (or the SNR gets lower). Thresholds for intermediate noise frames (e.g., when ns_est  216   a - b  does not exceed the noise estimate threshold but is above a minimum noise measure) may be adjusted by interpolating between the “clean” settings (Table 1) and “noise” settings (Table 2 and/or Table 3), based on the input noise estimate. Alternatively, hard threshold sets may be defined for some intermediate noise estimates. 
     The “voiced” voicing threshold may be adjusted independently of the “unvoiced” voicing and energy thresholds. For example, the “voiced” voicing threshold may be adjusted but neither the “unvoiced” voicing or energy thresholds may be adjusted. Alternatively, one or both of the “unvoiced” voicing and energy thresholds may be adjusted but the “voiced” voicing threshold may not be adjusted. Alternatively, the “voiced” voicing threshold may be adjusted with only one of the “unvoiced” voicing and energy thresholds. 
     Noisy speech is the same as clean speech with added noise. With adaptive periodicity threshold control, the robust speech classification technique may be more likely to produce identical classification decisions for clean and noisy speech than previously possible. When the nacf thresholds have been set for each frame, control flow proceeds to step  308 . 
     In step  308 , a speech mode classification  246   a - b  is determined based, at least in part, on the noise estimate. A state machine or any other method of analysis selected according to the signal environment is applied to the parameters. In one configuration, the parameters input from external components and the internally generated parameters are applied to a state based mode decision making process described in detail with reference to  FIGS. 4A-4C  and Tables 4-6. The decision making process produces a speech mode classification. In one configuration, a speech mode classification  246   a - b  of Transient, Up-Transient, Down Transient, Voiced, Unvoiced, or Silence is produced. When a speech mode decision  246   a - b  has been produced, control flow proceeds to step  310 . 
     In step  310 , state variables and various parameters are updated to include the current frame. In one configuration, vEav  236   a - b , vEprev  238   a - b , and the voiced state of the current frame are updated. The current frame energy E  230   a - b , nacf_at_pitch  226   a - b , and the current frame speech mode  246   a - b  are updated for classifying the next frame. Steps  302 - 310  may be repeated for each frame of speech. 
       FIGS. 4A-4C  illustrate configurations of the mode decision making process for noise-robust speech classification. The decision making process selects a state machine for speech classification based on the periodicity of the speech frame. For each frame of speech, a state machine most compatible with the periodicity, or noise component, of the speech frame is selected for the decision making process by comparing the speech frame periodicity measure, i.e. nacf_at_pitch value  226   a - b , to the NACF thresholds set in step  304  of  FIG. 3 . The level of periodicity of the speech frame limits and controls the state transitions of the mode decision process, producing a more robust classification. 
       FIG. 4A  illustrates one configuration of the state machine selected in one configuration when vad  220   a - b  is 1 (there is active speech) and the third value of nacf_at_pitch  226   a - b  (i.e. nacf_at_pitch[2], zero indexed) is very high, or greater than VOICEDTH. VOICEDTH is defined in step  306  of  FIG. 3 . Table 4 illustrates the parameters evaluated by each state: 
     
       
         
           
               
               
             
               
                   
                 TABLE 4 
               
             
            
               
                   
                   
               
               
                   
                 PREVIOUS 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                 UP- 
                   
                 DOWN- 
               
               
                 CURRENT 
                 SILENCE 
                 UNVOICED 
                 VOICED 
                 TRANSIENT 
                 TRANSIENT 
                 TRANSIENT 
               
               
                   
               
               
                 SILENCE 
                 Vad = 0 
                 nacf_ap[3] 
                 X 
                 DEFAULT 
                 X 
                 X 
               
               
                   
                   
                 very low, zcr 
               
               
                   
                   
                 high, bER low, 
               
               
                   
                   
                 vER very low 
               
               
                 UNVOICED 
                 Vad = 0 
                 nacf_ap[3] 
                 X 
                 DEFAULT 
                 X 
                 X 
               
               
                   
                   
                 very low, 
               
               
                   
                   
                 nacf_ap[4] 
               
               
                   
                   
                 very low, nacf 
               
               
                   
                   
                 very low, zcr 
               
               
                   
                   
                 high, bER low, 
               
               
                   
                   
                 vER very low, 
               
               
                   
                   
                 E &lt; vEprev 
               
               
                 VOICED 
                 Vad = 0 
                 vER very low, 
                 DEFAULT 
                 X 
                 nacf_ap[1] low, 
                 vER very low, 
               
               
                   
                   
                 E &lt; vEprev 
                   
                   
                 nacf_ap[3] low, 
                 nacf_ap[3] 
               
               
                   
                   
                   
                   
                   
                 E &gt; 0.5 * vEprev 
                 not too high, 
               
               
                 UP- 
                 Vad = 0 
                 vER very low, 
                 DEFAULT 
                 X 
                 nacf_ap[1] low, 
                 nacf_ap[3] 
               
               
                 TRANSIENT, 
                   
                 E &lt; vEprev 
                   
                   
                 nacf_ap[3] 
                 not too high, 
               
               
                 TRANSIENT 
                   
                   
                   
                   
                 not too high, 
                 E &gt; 0.05 * vEav 
               
               
                   
                   
                   
                   
                   
                 nacf_ap[4] low, 
               
               
                   
                   
                   
                   
                   
                 previous 
               
               
                   
                   
                   
                   
                   
                 classification 
               
               
                   
                   
                   
                   
                   
                 is not transient 
               
               
                 DOWN- 
                 Vad = 0 
                 vER very low, 
                 X 
                 X 
                 E &gt; vEprev 
                 DEFAULT 
               
               
                 TRANSIENT 
               
               
                   
               
            
           
         
       
     
     Table 4, in accordance with one configuration, illustrates the parameters evaluated by each state, and the state transitions when the third value of nacf_at_pitch  226   a - b  (i.e. nacf_at_pitch[2]) is very high, or greater than VOICEDTH. The decision table illustrated in Table 4 is used by the state machine described in  FIG. 4A . The speech mode classification  246   a - b  of the previous frame of speech is shown in the leftmost column. When parameters are valued as shown in the row associated with each previous mode, the speech mode classification transitions to the current mode identified in the top row of the associated column. 
     The initial state is Silence  450   a . The current frame will always be classified as Silence  450   a , regardless of the previous state, if vad=0 (i.e., there is no voice activity). 
     When the previous state is Silence  450   a , the current frame may be classified as either Unvoiced  452   a  or Up-Transient  460   a . The current frame is classified as Unvoiced  452   a  if nacf_at_pitch[3] is very low, zcr  228   a - b  is high, bER  234   a - b  is low and vER  240   a - b  is very low, or if a combination of these conditions are met. Otherwise the classification defaults to Up-Transient  460   a.    
     When the previous state is Unvoiced  452   a , the current frame may be classified as Unvoiced  452   a  or Up-Transient  460   a . The current frame remains classified as Unvoiced  452   a  if nacf  224   a - b  is very low, nacf_at_pitch[3] is very low, nacf_at_pitch[4] is very low, zcr  228   a - b  is high, bER  234   a - b  is low, vER  240   a - b  is very low, and E  230   a - b  is less than vEprev  238   a - b , or if a combination of these conditions are met. Otherwise the classification defaults to Up-Transient  460   a.    
     When the previous state is Voiced  456   a , the current frame may be classified as Unvoiced  452   a , Transient  454   a , Down-Transient  458   a , or Voiced  456   a . The current frame is classified as Unvoiced  452   a  if vER  240   a - b  is very low, and E  230   a  is less than vEprev  238   a - b . The current frame is classified as Transient  454   a  if nacf_at_pitch[1] and nacf_at_pitch[3] are low, E  230   a - b  is greater than half of vEprev  238   a - b , or a combination of these conditions are met. The current frame is classified as Down-Transient  458   a  if vER  240   a - b  is very low, and nacf_at_pitch[3] has a moderate value. Otherwise, the current classification defaults to Voiced  456   a.    
     When the previous state is Transient  454   a  or Up-Transient  460   a , the current frame may be classified as Unvoiced  452   a , Transient  454   a , Down-Transient  458   a  or Voiced  456   a . The current frame is classified as Unvoiced  452   a  if vER  240   a - b  is very low, and E  230   a - b  is less than vEprev  238   a - b . The current frame is classified as Transient  454   a  if nacf_at_pitch[1] is low, nacf_at_pitch[3] has a moderate value, nacf_at_pitch[4] is low, and the previous state is not Transient  454   a , or if a combination of these conditions are met. The current frame is classified as Down-Transient  458   a  if nacf_at_pitch[3] has a moderate value, and E  230   a - b  is less than 0.05 times vEav  236   a - b . Otherwise, the current classification defaults to Voiced  456   a - b.    
     When the previous frame is Down-Transient  458   a , the current frame may be classified as Unvoiced  452   a , Transient  454   a  or Down-Transient  458   a . The current frame will be classified as Unvoiced  452   a  if vER  240   a - b  is very low. The current frame will be classified as Transient  454   a  if E  230   a - b  is greater than vEprev 238   a - b . Otherwise, the current classification remains Down-Transient  458   a.    
       FIG. 4B  illustrates one configuration of the state machine selected in one configuration when vad  220   a - b  is 1 (there is active speech) and the third value of nacf_at_pitch  226   a - b  is very low, or less than UNVOICEDTH. UNVOICEDTH is defined in step  306  of  FIG. 3 . Table 5 illustrates the parameters evaluated by each state. 
     
       
         
           
               
               
             
               
                   
                 TABLE 5 
               
             
            
               
                   
                   
               
               
                   
                 PREVIOUS 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                   
                   
                 DOWN- 
               
               
                 CURRENT 
                 SILENCE 
                 UNVOICED 
                 VOICED 
                 UP-TRANSIENT 
                 TRANSIENT 
                 TRANSIENT 
               
               
                   
               
               
                 SILENCE 
                 Vad = 0 
                 DEFAULT 
                 X 
                 nacf_ap[2], 
                 X 
                 X 
               
               
                   
                   
                   
                   
                 nacf_ap[3] and 
               
               
                   
                   
                   
                   
                 nacf_ap[4] show 
               
               
                   
                   
                   
                   
                 increasing trend, 
               
               
                   
                   
                   
                   
                 nacf_ap[3] not too 
               
               
                   
                   
                   
                   
                 low, nacf_ap[4] not 
               
               
                   
                   
                   
                   
                 too low, zcr not too 
               
               
                   
                   
                   
                   
                 high, vER not too low, 
               
               
                   
                   
                   
                   
                 bER high, zcr very low 
               
               
                 UNVOICED 
                 Vad = 0 
                 DEFAULT 
                 X 
                 nacf_ap[2], 
                 X 
                 X 
               
               
                   
                   
                   
                   
                 nacf_ap[3] and 
               
               
                   
                   
                   
                   
                 nacf_ap[4] show 
               
               
                   
                   
                   
                   
                 increasing trend, 
               
               
                   
                   
                   
                   
                 nacf_ap[3] not too 
               
               
                   
                   
                   
                   
                 low, nacf_ap[4] not 
               
               
                   
                   
                   
                   
                 too low, zcr not too 
               
               
                   
                   
                   
                   
                 high, vER not too low, 
               
               
                   
                   
                   
                   
                 bER high, zcr very low, 
               
               
                   
                   
                   
                   
                 nacf_ap[3] very 
               
               
                   
                   
                   
                   
                 high, nacf_ap[4] 
               
               
                   
                   
                   
                   
                 very high, refl low, 
               
               
                   
                   
                   
                   
                 E &gt; vEprev, nacf not 
               
               
                   
                   
                   
                   
                 to low, etc. 
               
               
                 VOICED, 
                 Vad = 0 
                 bER &lt;= 0, 
                 X 
                 X 
                 bER &gt; 0, 
                 bER &gt; 0, 
               
               
                 UP- 
                   
                 vER very low, 
                   
                   
                 nacf_ap[2], 
                 nacf_ap[3], 
               
               
                 TRANSIENT, 
                   
                 E &lt; vEprev, 
                   
                   
                 nacf_ap[3] and 
                 not very high, 
               
               
                 TRANSIENT 
                   
                 bER &gt; 0 
                   
                   
                 nacf_ap[4] show 
                 vER2 &lt;− 15 
               
               
                   
                   
                   
                   
                   
                 increasing trend, 
               
               
                   
                   
                   
                   
                   
                 zcr not very high, 
               
               
                   
                   
                   
                   
                   
                 vER not too low, refl 
               
               
                   
                   
                   
                   
                   
                 low, nacf_ap[3] 
               
               
                   
                   
                   
                   
                   
                 not too low, nacf not 
               
               
                   
                   
                   
                   
                   
                 too low bER &lt;= 0 
               
               
                 DOWN- 
                 Vad = 0 
                 DEFAULT 
                 X 
                 X 
                 nacf_ap[2], 
                 vER not too low, 
               
               
                 TRANSIENT 
                   
                   
                   
                   
                 nacf_ap[3] and 
                 zcr low 
               
               
                   
                   
                   
                   
                   
                 nacf_ap[4] show 
               
               
                   
                   
                   
                   
                   
                 increasing trend, 
               
               
                   
                   
                   
                   
                   
                 nacf_ap[3] fairly high, 
               
               
                   
                   
                   
                   
                   
                 nacf_ap[4] fairly high, 
               
               
                   
                   
                   
                   
                   
                 vER not too low, 
               
               
                   
                   
                   
                   
                   
                 E &gt; 2*vEprev, etc. 
               
               
                   
               
            
           
         
       
     
     Table 5 illustrates, in accordance with one configuration, the parameters evaluated by each state, and the state transitions when the third value (i.e. nacf_at_pitch[2]) is very low, or less than UNVOICEDTH. The decision table illustrated in Table 5 is used by the state machine described in  FIG. 4B . The speech mode classification  246   a - b  of the previous frame of speech is shown in the leftmost column. When parameters are valued as shown in the row associated with each previous mode, the speech mode classification transitions to the current mode  246   a - b  identified in the top row of the associated column. 
     The initial state is Silence  450   b . The current frame will always be classified as Silence  450   b , regardless of the previous state, if vad=0 (i.e., there is no voice activity). 
     When the previous state is Silence  450   b , the current frame may be classified as either Unvoiced  452   b  or Up-Transient  460   b . The current frame is classified as Up-Transient  460   b  if nacf_at_pitch[2-4] show an increasing trend, nacf_at_pitch[3-4] have a moderate value, zcr  228   a - b  is very low to moderate, bER  234   a - b  is high, and vER  240   a - b  has a moderate value, or if a combination of these conditions are met. Otherwise the classification defaults to Unvoiced  452   b.    
     When the previous state is Unvoiced  452   b , the current frame may be classified as Unvoiced  452   b  or Up-Transient  460   b . The current frame is classified as Up-Transient  460   b  if nacf_at_pitch[2-4] show an increasing trend, nacf_at_pitch[3-4] have a moderate to very high value, zcr  228   a - b  is very low or moderate, vER  240   a - b  is not low, bER  234   a - b  is high, refl  222   a - b  is low, nacf  224   a - b  has moderate value and E  230   a - b  is greater than vEprev  238   a - b , or if a combination of these conditions is met. The combinations and thresholds for these conditions may vary depending on the noise level of the speech frame as reflected in the parameter ns_est  216   a - b  (or possibly multi-frame averaged SNR information  218 ). Otherwise the classification defaults to Unvoiced  452   b.    
     When the previous state is Voiced  456   b , Up-Transient  460   b , or Transient  454   b , the current frame may be classified as Unvoiced  452   b , Transient  454   b , or Down-Transient  458   b . The current frame is classified as Unvoiced  452   b  if bER  234   a - b  is less than or equal to zero, vER  240   a  is very low, bER  234   a - b  is greater than zero, and E  230   a - b  is less than vEprev  238   a - b , or if a combination of these conditions are met. The current frame is classified as Transient  454   b  if bER  234   a - b  is greater than zero, nacf_at_pitch[2-4] show an increasing trend, zcr  228   a - b  is not high, vER  240   a - b  is not low, refl  222   a - b  is low, nacf_at_pitch[3] and nacf  224   a - b  are moderate and bER  234   a - b  is less than or equal to zero, or if a certain combination of these conditions are met. The combinations and thresholds for these conditions may vary depending on the noise level of the speech frame as reflected in the parameter ns_est  216   a - b . The current frame is classified as Down-Transient  458   a - b  if, bER  234   a - b  is greater than zero, nacf_at_pitch[3] is moderate, E  230   a - b  is less than vEprev  238   a - b , zcr  228   a - b  is not high, and vER 2   242   a - b  is less then negative fifteen. 
     When the previous frame is Down-Transient  458   b , the current frame may be classified as Unvoiced  452   b , Transient  454   b  or Down-Transient  458   b . The current frame will be classified as Transient  454   b  if nacf_at_pitch[2-4] shown an increasing trend, nacf_at_pitch[3-4] are moderately high, vER  240   a - b  is not low, and E  230   a - b  is greater than twice vEprev  238   a - b , or if a combination of these conditions are met. The current frame will be classified as Down-Transient  458   b  if vER  240   a - b  is not low and zcr  228   a - b  is low. Otherwise, the current classification defaults to Unvoiced  452   b.    
       FIG. 4C  illustrates one configuration of the state machine selected in one configuration when vad  220   a - b  is 1 (there is active speech) and the third value of nacf_at_pitch  226   a - b  (i.e. nacf_at_pitch[3]) is moderate, i.e., greater than UNVOICEDTH and less than VOICEDTH. UNVOICEDTH and VOICEDTH are defined in step  306  of  FIG. 3 . Table 6 illustrates the parameters evaluated by each state. 
     
       
         
           
               
               
             
               
                   
                 TABLE 6 
               
             
            
               
                   
                   
               
               
                   
                 PREVIOUS 
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                   
                   
                   
                 UP- 
                   
                 DOWN- 
               
               
                 CURRENT 
                 SILENCE 
                 UNVOICED 
                 VOICED 
                 TRANSIENT 
                 TRANSIENT 
                 TRANSIENT 
               
               
                   
               
               
                 SILENCE 
                 Vad = 0 
                 DEFAULT 
                 X 
                 nacf_ap[2], 
                 X 
                 X 
               
               
                   
                   
                   
                   
                 nacf_ap[3] and 
               
               
                   
                   
                   
                   
                 nacf_ap[4] show 
               
               
                   
                   
                   
                   
                 increasing trend, 
               
               
                   
                   
                   
                   
                 nacf_ap[3] not too 
               
               
                   
                   
                   
                   
                 low, nacf_ap[4] not 
               
               
                   
                   
                   
                   
                 too low, zcr not too 
               
               
                   
                   
                   
                   
                 high, vER not too low, 
               
               
                   
                   
                   
                   
                 bER high, zcr very low 
               
               
                 UNVOICED 
                 Vad = 0 
                 DEFAULT 
                 X 
                 nacf_ap[2], 
                 X 
                 X 
               
               
                   
                   
                   
                   
                 nacf_ap[3] and 
               
               
                   
                   
                   
                   
                 nacf_ap[4] show 
               
               
                   
                   
                   
                   
                 increasing trend, 
               
               
                   
                   
                   
                   
                 nacf_ap[3] not too 
               
               
                   
                   
                   
                   
                 low, nacf_ap[4] not 
               
               
                   
                   
                   
                   
                 too low, zcr not too 
               
               
                   
                   
                   
                   
                 high, vER not too low, 
               
               
                   
                   
                   
                   
                 bER high, zcr very low, 
               
               
                   
                   
                   
                   
                 nacf_ap[3] very 
               
               
                   
                   
                   
                   
                 high, nacf_ap[4] 
               
               
                   
                   
                   
                   
                 very high, refl low, 
               
               
                   
                   
                   
                   
                 E &gt; vEprev, nacf 
               
               
                   
                   
                   
                   
                 not to low, etc. 
               
               
                 VOICED, 
                 Vad = 0 
                 bER &lt;= 0, 
                 X 
                 X 
                 bER &gt; 0, 
                 bER &gt; 0, 
               
               
                 UP- 
                   
                 vER very low, 
                   
                   
                 nacf_ap[2], 
                 nacf_ap[3], 
               
               
                 TRANSIENT, 
                   
                 E &lt; vEprev, 
                   
                   
                 nacf_ap[3] and 
                 not very high, 
               
               
                 TRANSIENT 
                   
                 bER &gt; 0 
                   
                   
                 nacf_ap[4] show 
                 vER2 &lt;− 15 
               
               
                   
                   
                   
                   
                   
                 increasing trend, 
               
               
                   
                   
                   
                   
                   
                 zcr not very high, vER 
               
               
                   
                   
                   
                   
                   
                 not too low, refl low, 
               
               
                   
                   
                   
                   
                   
                 nacf_ap[3] 
               
               
                   
                   
                   
                   
                   
                 not too low, nacf not 
               
               
                   
                   
                   
                   
                   
                 too low bER &lt;= 0 
               
               
                 DOWN- 
                 Vad = 0 
                 DEFAULT 
                 X 
                 X 
                 nacf_ap[2], 
                 vER not too 
               
               
                 TRANSIENT 
                   
                   
                   
                   
                 nacf_ap[3] and 
                 low, zcr low 
               
               
                   
                   
                   
                   
                   
                 nacf_ap[4] show 
               
               
                   
                   
                   
                   
                   
                 increasing trend, 
               
               
                   
                   
                   
                   
                   
                 nacf_ap[3] fairly high, 
               
               
                   
                   
                   
                   
                   
                 nacf_ap[4] fairly high, 
               
               
                   
                   
                   
                   
                   
                 vER not too low, 
               
               
                   
                   
                   
                   
                   
                 E &gt; 2*vEprev, etc. 
               
               
                   
               
            
           
         
       
     
     Table 6 illustrates, in accordance with one embodiment, the parameters evaluated by each state, and the state transitions when the third value of nacf_at_pitch  226   a - b  (i.e. nacf_at_pitch[3]) is moderate, i.e., greater than UNVOICEDTH but less than VOICEDTH. The decision table illustrated in Table 6 is used by the state machine described in  FIG. 4C . The speech mode classification of the previous frame of speech is shown in the leftmost column. When parameters are valued as shown in the row associated with each previous mode, the speech mode classification  246   a - b  transitions to the current mode  246   a - b  identified in the top row of the associated column. 
     The initial state is Silence  450   c . The current frame will always be classified as Silence  450   c , regardless of the previous state, if vad=0 (i.e., there is no voice activity). 
     When the previous state is Silence  450   c , the current frame may be classified as either Unvoiced  452   c  or Up-transient  460   c . The current frame is classified as Up-Transient  460   c  if nacf_at_pitch[2-4] shown an increasing trend, nacf_at_pitch[3-4] are moderate to high, zcr  228   a - b  is not high, bER  234   a - b  is high, vER  240   a - b  has a moderate value, zcr  228   a - b  is very low and E  230   a - b  is greater than twice vEprev  238   a - b , or if a certain combination of these conditions are met. Otherwise the classification defaults to Unvoiced  452   c.    
     When the previous state is Unvoiced  452   c , the current frame may be classified as Unvoiced  452   c  or Up-Transient  460   c . The current frame is classified as Up-Transient  460   c  if nacf_at_pitch[2-4] shown an increasing trend, nacf_at_pitch[3-4] have a moderate to very high value, zcr  228   a - b  is not high, vER  240   a - b  is not low, bER  234   a - b  is high, refl  222   a - b  is low, E  230   a - b  is greater than vEprev  238   a - b , zcr  228   a - b  is very low, nacf  224   a - b  is not low, maxsfe_idx  244   a - b  points to the last subframe and E  230   a - b  is greater than twice vEprev  238   a - b , or if a combination of these conditions are met. The combinations and thresholds for these conditions may vary depending on the noise level of the speech frame as reflected in the parameter ns_est  216   a - b  (or possibly multi-frame averaged SNR information  218 ). Otherwise the classification defaults to Unvoiced  452   c.    
     When the previous state is Voiced  456   c , Up-Transient  460   c , or Transient 454   c , the current frame may be classified as Unvoiced  452   c , Voiced  456   c , Transient  454   c , Down-Transient  458   c . The current frame is classified as Unvoiced  452   c  if bER  234   a - b  is less than or equal to zero, vER  240   a - b  is very low, Enext  232   a - b  is less than E  230   a - b , nacf_at_pitch[3-4] are very low, bER  234   a - b  is greater than zero and E  230   a - b  is less than vEprev  238   a - b , or if a certain combination of these conditions are met. The current frame is classified as Transient  454   c  if bER  234   a - b  is greater than zero, nacf_at_pitch[2-4] show an increasing trend, zcr  228   a - b  is not high, vER  240   a - b  is not low, refl  222   a - b  is low, nacf_at_pitch[3] and nacf  224   a - b  are not low, or if a combination of these conditions are met. The combinations and thresholds for these conditions may vary depending on the noise level of the speech frame as reflected in the parameter ns_est  216   a - b  (or possibly multi-frame averaged SNR information  218 ). The current frame is classified as Down-Transient  458   c  if, bER  234   a - b  is greater than zero, nacf_at_pitch[3] is not high, E  230   a - b  is less than vEprev  238   a - b , zcr  228   a - b  is not high, vER  240 - ab  is less than negative fifteen and vER 2   242   a - b  is less then negative fifteen, or if a combination of these conditions are met. The current frame is classified as Voiced  456   c  if nacf_at_pitch[2] is greater than LOWVOICEDTH, bER  234   a - b  is greater than or equal to zero, and vER  240   a - b  is not low, or if a combination of these conditions are met. 
     When the previous frame is Down-Transient  458   c , the current frame may be classified as Unvoiced  452   c , Transient  454   c  or Down-Transient  458   c . The current frame will be classified as Transient  454   c  if bER  234   a - b  is greater than zero, nacf_at_pitch[2-4] show an increasing trend, nacf_at_pitch[3-4] are moderately high, vER  240   a - b  is not low, and E  230   a - b  is greater than twice vEprev  238   a - b , or if a certain combination of these conditions are met. The current frame will be classified as Down-Transient  458   c  if vER  240   a - b  is not low and zcr  228   a - b  is low. Otherwise, the current classification defaults to Unvoiced  452   c.    
       FIG. 5  is a flow diagram illustrating a method  500  for adjusting thresholds for classifying speech. The adjusted thresholds (e.g., NACF, or periodicity, thresholds) may then be used, for example, in the method  300  of noise-robust speech classification illustrated in  FIG. 3 . The method  500  may be performed by the speech classifiers  210   a - b  illustrated in  FIGS. 2A-2B . 
     A noise estimate (e.g., ns_est  216   a - b ), of input speech may be received  502  at the speech classifier  210   a - b . The noise estimate may be based on multiple frames of input speech. Alternatively, an average of multi-frame SNR information  218  may be used instead of a noise estimate. Any suitable noise metric that is relatively stable over multiple frames may be used in the method  500 . The speech classifier  210   a - b  may determine  504  whether the noise estimate exceeds a noise estimate threshold. Alternatively, the speech classifier  210   a - b  may determine if the multi-frame SNR information  218  fails to exceed a multi-frame SNR threshold. If not, the speech classifier  210   a - b  may not  506  adjust any NACF thresholds for classifying speech as either “voiced” or “unvoiced.” However, if the noise estimate exceeds the noise estimate threshold, the speech classifier  210   a - b  may also determine  508  whether to adjust the unvoiced NACF thresholds. If no, the unvoiced NACF thresholds may not  510  be adjusted, i.e., the thresholds for classifying a frame as “unvoiced” may not be adjusted. If yes, the speech classifier  210   a - b  may increase  512  the unvoiced NACF thresholds, i.e., increase a voicing threshold for classifying a current frame as unvoiced and increase an energy threshold for classifying the current frame as unvoiced. Increasing the voicing threshold and the energy threshold for classifying a frame as “unvoiced” may make it easier (i.e., more permissive) to classify a frame as unvoiced as the noise estimate gets higher (or the SNR gets lower). The speech classifier  210   a - b  may also determine  514  whether to adjust the voiced NACF threshold (alternatively, spectral tilt or transient detection or zero-crossing rate thresholds may be adjusted). If no, the speech classifier  210   a - b  may not  516  adjust the voicing threshold for classifying a frame as “voiced,” i.e., the thresholds for classifying a frame as “voiced” may not be adjusted. If yes, the speech classifier  210   a - b  may decrease  518  a voicing threshold for classifying a current frame as “voiced.” Therefore, the NACF thresholds for classifying a speech frame as either “voiced” or “unvoiced” may be adjusted independently of each other. For example, depending on how the classifier  610  is tuned in the clean (no noise) case, only one of the “voiced” or “unvoiced” thresholds may be adjusted independently, i.e., it can be the case that the “unvoiced” classification is much more sensitive to the noise. Furthermore, the penalty for misclassifying a “voiced” frame may be bigger than for misclassifying an “unvoiced” frame (both in terms of quality and bit rate). 
       FIG. 6  is a block diagram illustrating a speech classifier  610  for noise-robust speech classification. The speech classifier  610  may correspond to the speech classifiers  210   a - b  illustrated in  FIGS. 2A-2B  and may perform the method  300  illustrated in  FIG. 3  or the method  500  illustrated in  FIG. 5 . 
     The speech classifier  610  may include received parameters  670 . This may include received speech frames (t_in)  672 , SNR information  618 , a noise estimate (ns_est)  616 , voice activity information (vad)  620 , reflection coefficients (refl)  622 , NACF  624  and NACF around pitch (nacf_at_pitch)  626 . These parameters  670  may be received from various modules such as those illustrated in  FIGS. 2A-2B . For example, the received speech frames (t_in)  672  may be the output speech frames  214   a  from a noise suppressor  202  illustrated in  FIG. 2A  or the input speech  212   b  itself as illustrated in  FIG. 2   b.    
     A parameter derivation module  674  may also determine a set of derived parameters  682 . Specifically, the parameter derivation module  674  may determine a zero crossing rate (zcr)  628 , a current frame energy (E)  630 , a look ahead frame energy (Enext)  632 , a band energy ratio (bER)  634 , a three frame average voiced energy (vEav)  636 , a previous frame energy (vEprev)  638 , a current energy to previous three-frame average voiced energy ratio (vER)  640 , a current frame energy to three-frame average voiced energy (vER 2 )  642  and a max sub-frame energy index (maxsfe_idx)  644 . 
     A noise estimate comparator  678  may compare the received noise estimate (ns_est)  616  with a noise estimate threshold  676 . If the noise estimate (ns_est)  616  does not exceed the noise estimate threshold  676 , a set of NACF thresholds  684  may not be adjusted. However, if the noise estimate (ns_est)  616  exceeds the noise estimate threshold  676  (indicating the presence of high noise), one or more of the NACF thresholds  684  may be adjusted. Specifically, a voicing threshold for classifying “voiced” frames  686  may be decreased, a voicing threshold for classifying “unvoiced” frames  688  may be increased, an energy threshold for classifying “unvoiced” frames  690  may be increased, or some combination of adjustments. Alternatively, instead of comparing the noise estimate (ns_est)  616  to the noise estimate threshold  676 , the noise estimate comparator may compare SNR information  618  to a multi-frame SNR threshold  680  to determine whether to adjust the NACF thresholds  684 . In that configuration, the NACF thresholds  684  may be adjusted if the SNR information  618  fails to exceed the multi-frame SNR threshold  680 , i.e., the NACF thresholds  684  may be adjusted when the SNR information  618  falls below a minimum level, thus indicating the presence of high noise. Any suitable noise metric that is relatively stable across multiple frames may be used by the noise estimate comparator  678 . 
     A classifier state machine  692  may then be selected and used to determine a speech mode classification  646  based at least, in part, on the derived parameters  682 , as described above and illustrated in  FIGS. 4A-4C  and Tables 4-6. 
       FIG. 7  is a timeline graph illustrating one configuration of a received speech signal  772  with associated parameter values and speech mode classifications  746 . Specifically,  FIG. 7  illustrates one configuration of the present systems and methods in which the speech mode classification  746  is chosen based on various received parameters  670  and derived parameters  682 . Each signal or parameter is illustrated in  FIG. 7  as a function of time. 
     For example, the third value of NACF around pitch (nacf_at_pitch[2])  794 , the fourth value of NACF around pitch (nacf_at_pitch[3])  795  and the fifth value of NACF around pitch (nacf_at_pitch[4])  796  are shown. Furthermore, the current energy to previous three-frame average voiced energy ratio (vER)  740 , band energy ratio (bER)  734 , zero crossing rate (zcr)  728  and reflection coefficients (refl)  722  are also shown. Based on the illustrated signals, the received speech  772  may be classified as Silence around time  0 , Unvoiced around time  4 , Transient around time  9 , Voiced around time  10  and Down-Transient around time  25 . 
       FIG. 8  illustrates certain components that may be included within an electronic device/wireless device  804 . The electronic device/wireless device  804  may be an access terminal, a mobile station, a user equipment (UE), a base station, an access point, a broadcast transmitter, a node B, an evolved node B, etc. The electronic device/wireless device  804  includes a processor  803 . The processor  803  may be a general purpose single- or multi-chip microprocessor (e.g., an ARM), a special purpose microprocessor (e.g., a digital signal processor (DSP)), a microcontroller, a programmable gate array, etc. The processor  803  may be referred to as a central processing unit (CPU). Although just a single processor  803  is shown in the electronic device/wireless device  804  of  FIG. 8 , in an alternative configuration, a combination of processors (e.g., an ARM and DSP) could be used. 
     The electronic device/wireless device  804  also includes memory  805 . The memory  805  may be any electronic component capable of storing electronic information. The memory  805  may be embodied as random access memory (RAM), read-only memory (ROM), magnetic disk storage media, optical storage media, flash memory devices in RAM, on-board memory included with the processor, EPROM memory, EEPROM memory, registers, and so forth, including combinations thereof. 
     Data  807   a  and instructions  809   a  may be stored in the memory  805 . The instructions  809   a  may be executable by the processor  803  to implement the methods disclosed herein. Executing the instructions  809   a  may involve the use of the data  807   a  that is stored in the memory  805 . When the processor  803  executes the instructions  809   a , various portions of the instructions  809   b  may be loaded onto the processor  803 , and various pieces of data  807   b  may be loaded onto the processor  803 . 
     The electronic device/wireless device  804  may also include a transmitter  811  and a receiver  813  to allow transmission and reception of signals to and from the electronic device/wireless device  804 . The transmitter  811  and receiver  813  may be collectively referred to as a transceiver  815 . Multiple antennas  817   a - b  may be electrically coupled to the transceiver  815 . The electronic device/wireless device  804  may also include (not shown) multiple transmitters, multiple receivers, multiple transceivers and/or additional antennas. 
     The electronic device/wireless device  804  may include a digital signal processor (DSP)  821 . The electronic device/wireless device  804  may also include a communications interface  823 . The communications interface  823  may allow a user to interact with the electronic device/wireless device  804 . 
     The various components of the electronic device/wireless device  804  may be coupled together by one or more buses, which may include a power bus, a control signal bus, a status signal bus, a data bus, etc. For the sake of clarity, the various buses are illustrated in  FIG. 8  as a bus system  819 . 
     The techniques described herein may be used for various communication systems, including communication systems that are based on an orthogonal multiplexing scheme. Examples of such communication systems include Orthogonal Frequency Division Multiple Access (OFDMA) systems, Single-Carrier Frequency Division Multiple Access (SC-FDMA) systems, and so forth. An OFDMA system utilizes orthogonal frequency division multiplexing (OFDM), which is a modulation technique that partitions the overall system bandwidth into multiple orthogonal sub-carriers. These sub-carriers may also be called tones, bins, etc. With OFDM, each sub-carrier may be independently modulated with data. An SC-FDMA system may utilize interleaved FDMA (IFDMA) to transmit on sub-carriers that are distributed across the system bandwidth, localized FDMA (LFDMA) to transmit on a block of adjacent sub-carriers, or enhanced FDMA (EFDMA) to transmit on multiple blocks of adjacent sub-carriers. In general, modulation symbols are sent in the frequency domain with OFDM and in the time domain with SC-FDMA. 
     The term “determining” encompasses a wide variety of actions and, therefore, “determining” can include calculating, computing, processing, deriving, investigating, looking up (e.g., looking up in a table, a database or another data structure), ascertaining and the like. Also, “determining” can include receiving (e.g., receiving information), accessing (e.g., accessing data in a memory) and the like. Also, “determining” can include resolving, selecting, choosing, establishing and the like. 
     The phrase “based on” does not mean “based only on,” unless expressly specified otherwise. In other words, the phrase “based on” describes both “based only on” and “based at least on.” 
     The term “processor” should be interpreted broadly to encompass a general purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, and so forth. Under some circumstances, a “processor” may refer to an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. The term “processor” may refer to a combination of processing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. 
     The term “memory” should be interpreted broadly to encompass any electronic component capable of storing electronic information. The term memory may refer to various types of processor-readable media such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, etc. Memory is said to be in electronic communication with a processor if the processor can read information from and/or write information to the memory. Memory that is integral to a processor is in electronic communication with the processor. 
     The terms “instructions” and “code” should be interpreted broadly to include any type of computer-readable statement(s). For example, the terms “instructions” and “code” may refer to one or more programs, routines, sub-routines, functions, procedures, etc. “Instructions” and “code” may comprise a single computer-readable statement or many computer-readable statements. 
     The functions described herein may be implemented in software or firmware being executed by hardware. The functions may be stored as one or more instructions on a computer-readable medium. The terms “computer-readable medium” or “computer-program product” refers to any tangible storage medium that can be accessed by a computer or a processor. By way of example, and not limitation, a computer-readable medium may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray® disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. 
     The methods disclosed herein comprise one or more steps or actions for achieving the described method. The method steps and/or actions may be interchanged with one another without departing from the scope of the claims. In other words, unless a specific order of steps or actions is required for proper operation of the method that is being described, the order and/or use of specific steps and/or actions may be modified without departing from the scope of the claims. 
     Further, it should be appreciated that modules and/or other appropriate means for performing the methods and techniques described herein, such as those illustrated by  FIGS. 3 and 5 , can be downloaded and/or otherwise obtained by a device. For example, a device may be coupled to a server to facilitate the transfer of means for performing the methods described herein. Alternatively, various methods described herein can be provided via a storage means (e.g., random access memory (RAM), read only memory (ROM), a physical storage medium such as a compact disc (CD) or floppy disk, etc.), such that a device may obtain the various methods upon coupling or providing the storage means to the device. 
     It is to be understood that the claims are not limited to the precise configuration and components illustrated above. Various modifications, changes and variations may be made in the arrangement, operation and details of the systems, methods, and apparatus described herein without departing from the scope of the claims.