EP0614075B1 - Verfahren und Vorrichtung zur Sprachkodierung mit Trellis-kodierter Quantisierung für LPC- Quantisierung - Google Patents

Verfahren und Vorrichtung zur Sprachkodierung mit Trellis-kodierter Quantisierung für LPC- Quantisierung Download PDF

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EP0614075B1
EP0614075B1 EP94103204A EP94103204A EP0614075B1 EP 0614075 B1 EP0614075 B1 EP 0614075B1 EP 94103204 A EP94103204 A EP 94103204A EP 94103204 A EP94103204 A EP 94103204A EP 0614075 B1 EP0614075 B1 EP 0614075B1
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quantization
state
lsp
trellis
fact
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EP0614075A2 (de
EP0614075A3 (de
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Marco Fratti
Silvio Cucchi
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Alcatel Lucent SAS
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Alcatel CIT SA
Alcatel SA
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/06—Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/002—Dynamic bit allocation

Definitions

  • the present invention relates to a method for speech coding as set forth in the preamble of claim 1 and a speech coder as set forth in the preamble of claim 19.
  • Trellis Coded Modulation From the field of the communication theory, and in particular from the modulation theory the Trellis Coded Modulation technique is well known.
  • the Trellis Coded Modulation paradigm combined with well-known quantization theories, gave rise to the Trellis Coded Quantization (TCQ) algorithm.
  • a speech coder that employs vector quantization of LPC parameters after conversion from RC to LSP.
  • the gain and pitch are encoded using an adaptive tracking technique with a form of trellis coding.
  • Said form of trellis coding involves a too high computational load, since the generated sequences are compared to find the minimum distortion.
  • TCQ is a recent technique for efficient scalar encoding of any source.
  • the main object of the present invention is therefore substantially an efficient and effective way how to apply the TCQ technique.
  • the method for speech coding is constructed as set forth in claims 1 or 18 and the speech coder as set forth in claim 19.
  • figure 1 is showing a general structure of a Trellis Coded Quantizer
  • figure 2 is a Trellis Coded Quantizer with variable bit allocation
  • figure 3 is a TCQ scheme for LSP difference quantization
  • figure 4 is an updated paths in the TCQ.
  • the trellis encoder is completely specified by:
  • the 4-state trellis is fully connected.
  • the number associated to each state transition (branch) represents the quantization value corresponding to that branch.
  • the trellis is a N-stage one, that is, it is employed for coding a N-component input vector.
  • a trellis scheme like the one in Figure 1 is an example of what is generally found in the literature. That is, the topological configuration of the trellis is the same at each quantization step.
  • the quantization level number is the same at each quantization step.
  • This configuration may be not the ideal one in case one needs to quantize a vector whose scalar components have a different 'importance scale', according to a predefined performance criterium. In this case, a different bit/sample number may be necessary for each vector component.
  • both the bit/sample increase and the decrease can be easily realized while carrying out the Viterbi algorithm in the encoding process.
  • the addition of parallel transitions implies an increase in the evaluation of the local transition state metrics.
  • pruning a state transition branch implies the assignment of a corresponding 'infinite' local transition state metric.
  • LSP line spectrum pairs
  • the ordering property of the LSP parameters can be exploited by quantizing the differences between adjacent LSP frequencies instead of the absolute values of the LSP frequencies.
  • a proper bit allocation can be assigned to each LSP difference according to its perceptual importance.
  • the TCQ algorithm When applied to the quantization of the LSP differences, the TCQ algorithm proves itself to be particularly effective, since the quantization error accumulated in quantizing the - say - first ( i - 1)-th LSP differences can be taken into account in the search of the optimum quantization level for the i -th LSP difference.
  • Each trellis state will be assigned a "history path", at each i -th trellis stage; each state transition belonging to this history path will correspond to a pointer to the quantization level of the corresponding LSP difference.
  • the i -th quantized LSP can be reconstructed (note that this reconstructed LSP will be different - in general - for each trellis state).
  • the quantity D i / j refers to the quantized i -th LSP difference, according to the corresponding quantization level belonging to the j -th state history path.
  • LSP 1 D 0 1 + D 1 1 be the reconstructed LSP along the state 1 path.
  • LSP 1 D 0 1 + D 1 1 be the reconstructed LSP along the state 2 path.
  • LSP 3 D 0 3 + D 1 3 be the reconstructed LSP along the state 3 path.
  • transition cost from each j -th state to each possible k -th future state is computed.
  • This transition cost is related to the quantization level associated to the corresponding transition branch; with reference to Figure 3, the transition cost is denoted as C jk .
  • C jk depends on the quantization error which is measured (according to a proper metric) as function of the "transitional" quantization level.
  • the state paths are updated as depicted in Figure 4. Furthermore, the overall accumulated costs are updated for each trellis state.
  • the final state with the minimum accumulated cost is selected as the "winning" one. Its index (or, equivalently, the index of the corresponding initial state) is transmitted, together with the state transition labels of its history path.
  • the initial winning state index and the state transition labels of its history path are input. All the LSP differences can be recovered from the state transition label pointers to the quantization level table. Afterward, the LSP frequencies can be reconstructed.
  • the working principle is analogous to the one described previously for the 1-D (i.e. intra-frame) case, which simply exploits the LSP,ordering property.
  • predictor length is not necessarily the same in each dimension.
  • f i ( n ) a i f i -1 ( n ) + b i f i ( n -1) + c i f i -1 ( n -1) that is, we introduce another inter-frame/intra-frame dependency, namely the one related to the previous (in the intra-frame sense) LSP of the previous (in the inter-frame sense) frame.
  • the third weighting coefficient can be determined in an 'optimal' way, as will be described in a following section.
  • the concept can be extended further, by introducing a multi-coefficient multi-dimensional predictor, operating with different prediction orders, according to the prediction 'direction' (i.e. intra-frame, inter-frame, various intra-frame/inter-frame combinations).
  • LSP differences can be recovered from the best state information and the related history path.
  • the LSP values can then be reconstructed by re-adding the previously (both in the intra-frame and in the inter-frame sense) reconstructed parameters, after weighting them by the corresponding predictor coefficients.
  • TCQ of the LSP parameters in a generical differential sense
  • TCQ of the LSP parameters can be carried out according to any suitable metric that allows to measure an overall distortion as function of successive local distortions.
  • a simple mean squared error ( D k j - L jk ) 2
  • a weighted MSE could be employed, following (e.g.) the guidelines specified in K.K. Paliwal, B.S. Atal, "Efficient Vector Quantization of LPC Parameters at 24 Bits/Frame", Proc. ICASSP '91, p. 661-664, where the spectral content of the speech signal at the LSP frequency location is taken into account explicitly.
  • a WMSE criterion that considers the relative weight of the specific LSP that is being quantized could also be considered.
  • a recursive structure may be used for the computation of the reflection coefficients, starting either from the values of the autocorrelation function (and thereby using the well-known Leroux-Gueguen algorithm) or from the values of the signal covariance function (by employing the so-called covariance-lattice formulation, as explained in A. Cumani, "On a Covariance-Lattice Algorithm for Linear Prediction", Proc. ICASSP '82, pagg. 651-654).
  • Leroux-Gueguen algorithm should be reformulated properly in order to take into account the eventual quantization of the reflection coefficients after their computation at each step of the recursion.
  • each reflection coefficient can be computed as function of each particular state.
  • the computed reflection coefficient can be quantized according to the quantization level subset 'seen' by each particular trellis state.
  • the recursive algorithm for reflection coefficient computation, with embedded TCQ may be stated as follows (only the formulation related to the covariance-lattice approach is given, since the corresponding formalism for the autocorrelation approach may be derived in an analogous way; also, note that the formalism used resembles the one described in: A. Cumani, "On a Covariance-Lattice Algorithm for Linear Prediction", Proc. ICASSP '82, pagg. 651-654.
  • a proper metric should be employed to carry out the quantization process; in particular, a matric that allows to measure an overall distortion as function of successive local distortions (such as a MSE- or WMSE-based metric) can be suitable.
  • the quantization procedures outlined in the previous paragraphs may not be the optimal ones (with respect to both the LSP and the reflection coefficients).
  • the i-th LSP difference must be quantized; its value is computed by taking the difference between the i-th LSF and the reconstructed (i.e. quantized) ( i - 1)-th LSF.
  • This reconstructed ( i - 1)-th LSF is different for each trellis state; the i -th LSF difference thus obtained must be quantized accordingly to the level partition "seen" by the corresponding trellis state.
  • TCQ training procedure in particular we start from a unique set of quantization values for each state subset; these values can be found by using a standard scalar quantization clustering procedure.
  • each possible quantization path is assigned a partition; the corresponding "cluster vector" can be derived by simply taking a proper mean of each partition value and assigning this mean value to the corresponding path state.
  • the LSF value training sequence is again input to the TCQ; a new partition set can be generated and the corresponding set of cluster vectors can be found.
  • Trellis Coded Vector Quantization is a generalization of the TCQ concept. It has been introduced in T.G. Fisher, M.W. Marcellin, M.Wang, "Trellis Coded Vector Quantization", IEEE Trans. on Information Theory, Vol. IT-37, Nov. 1991. and, again, consists of using a structured codebook with an expanded set of quantization levels.
  • the trellis structure prunes the expanded number of quantization reproduction vectors down to the desired encoding rate.
  • the TCVQ procedure is carried out in exactly the same way as for the TCQ counterpart, both in the 1-D case (i.e., taking into account only the intra-frame dependency of the LSP parameters), and in the case of multi-dimensional prediction (i.e., exploiting both the intra-frame and the inter-frame dependency of LSP parameters, with any prediction length in either direction).
  • the TCVQ procedure can be carried out by recursive quantization of reflection coefficient couples (if the subvector dimension is actually 2), by using the same strategy employed for the TCQ case.
  • TCVQ generalization for the LSP multi-dimensional predictor case and for the reflection coefficients case can be derived in a straightforward manner following the corresponding TCQ descriptions.
  • the trellis level reoptimization procedure can be carried out in an analogous way as for the TCQ case.
  • the vector clusters can be constructed in an iterative way, as function of the different trellis states and of the corresponding encoding paths. These clusters are obtained as 'centroid' (according to a predetermined metric) of corresponding partitions of the input vector set.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Human Computer Interaction (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
  • Error Detection And Correction (AREA)
  • Transmission Systems Not Characterized By The Medium Used For Transmission (AREA)

Claims (19)

  1. Verfahren zur Sprachcodierung, das die Schritte umfasst:
    Empfangen einer Menge von LPC-Filterkoeffizienten am Eingang;
    Quantisieren der LPC-Filterkoeffizientenmenge mittels Vektorquantisierung;
    dadurch gekennzeichnet, dass es ferner die Schritte umfasst
    Erzeugen einer erweiterten Menge von Quantisierungsebenen; Beschneiden der Menge von Quantisierungsebenen unter Verwendung einer TCQ-Technik.
  2. Verfahren nach Anspruch 1, gekennzeichnet durch eine variable Bitzuordnung bei jedem Quantisierungsschritt.
  3. Verfahren nach Anspruch 2, dadurch gekennzeichnet, dass eine Bitratenerhöhung durch Hinzufügen eines oder mehrerer paralleler Übergänge in den Zustandszweigen erhalten wird, wenn eine bestimmte Trellis-Topologie gegeben ist.
  4. Verfahren nach Anspruch 2, dadurch gekennzeichnet, dass eine Bitratenverringerung erhalten wird, indem einer oder mehrere Zustandszweige gelöscht werden, wenn eine bestimmte Trellis-Topologie gegeben ist.
  5. Verfahren nach Anspruch 1, dadurch gekennzeichnet, dass von jedem Quantisierungsschritt der beim Quantisieren der vorherigen Schritte aufgelaufene Quantisierungsfehler überwacht und schließlich kompensiert werden kann.
  6. Verfahren nach Anspruch 1, gekennzeichnet durch die LPC-Filterkoeffizienten, die die LSP-Parameter sind.
  7. Verfahren nach den Ansprüchen 5 und 6, dadurch gekennzeichnet, dass bei jedem Quantisierungsschritt jedem Trellis-Zustand ein Pfad der geschichtlichen Entwicklung zugewiesen wird, wobei jeder Pfadzweig einem Zeiger zur Quantisierungsebene des entsprechenden LSP-Wertes entspricht.
  8. Verfahren nach Anspruch 7, dadurch gekennzeichnet, dass der Pfad der geschichtlichen Entwicklung die zu jedem Quantisierungsschritt gehörige Information enthält.
  9. Verfahren nach Anspruch 6, dadurch gekennzeichnet, dass die rahmeninterne Korrelation beim Quantisieren der LSP-Parameter mittels eindimensionaler differentieller Vorhersagemethoden entlang der Frequenzrichtung ausgenutzt werden kann.
  10. Verfahren nach Anspruch 6, dadurch gekennzeichnet, dass die Zwischenrahmenkorrelation beim Quantisieren der LSP-Parameter mittels eindimensionaler differentieller Vorhersagemethoden entlang der Zeitrichtung ausgenutzt werden kann.
  11. Verfahren nach Anspruch 6, dadurch gekennzeichnet, dass sowohl die rahmeninterne Korrelation als auch die Zwischenrahmenkorrelation beim Quantisieren der LSP-Parameter mittels mehrdimensionaler differentieller Vorhersagemethoden ausgenutzt werden kann.
  12. Verfahren nach Anspruch 1, gekennzeichnet durch die LPC-Filterkoeffizienten, die die RC-Parameter sind.
  13. Verfahren nach den Ansprüche 12 und 5, dadurch gekennzeichnet, dass es die Schritte des Definierens einer Trellis-Topologie, des Definierens einer Anzahl von Quantisierungsebenen bei jedem Quantisierungsschritt und des Berechnens jedes RC als Funktion jedes besonderen Zustands umfasst.
  14. Verfahren nach Anspruch 1, dadurch gekennzeichnet, dass der Quantisierungsfehler unter Verwendung einer Metrik berechnet wird, die die Eigenschaft hat, dass sie bei jedem Quantisierungsschritt additiv ist.
  15. Verfahren nach Anspruch 14, dadurch gekennzeichnet, dass es die Schritte des Rekonstruierens des Codierpfades in Übereinstimmung mit jedem Trellis-Zustand, des Erhaltens eines Satzes von quantisierten LPC-Parametervektoren, des Erhaltens einer entsprechenden Darstellung in Form von LPC-Cepstrumkoeffizienten für jeden Vektor, des Messens des cepstralen Abstandes hinsichtlich der Cepstrum-Koeffizientendarstellung des nicht quantisierten Modells, des Auswählens der Trellis-Parameter, die den LPC-Vektor mit dem optimalen cepstralen Abstand definieren, umfasst.
  16. Verfahren nach Anspruch 1, gekennzeichnet durch die Übernahme einer TCQ-Schulungsprozedur für eine erneute Optimierung der Quantisierungsebenen.
  17. Verfahren nach Anspruch 16, dadurch gekennzeichnet, dass es ferner die Schritte umfasst: Ausgehen von einer Menge von Quantisierungswerten für jede Zustandsuntermenge des Trellis, Übernahme einer iterativen Prozedur mit einer Schulungsfolge von LSP als Eingabe, Zuweisung einer dem erhaltenen TCQ-Pfad entsprechenden Verteilung zu jedem eingegebenen LSP-Vektor, Nehmen eines Mittelwerts jedes Verteilungswertes,Zuweisen des Mittelwertes zu dem entsprechenden Zustandszweig des Pfades.
  18. Verfahren zur Sprachcodierung, das die Schritte umfasst:
    Empfangen einer Menge von LPC-Filterkoeffizienten am Eingang;
    Quantisieren der Menge der LPC-Filterkoeffizienten mittels Vektorquantisierung,
    dadurch gekennzeichnet, dass es ferner die Schritte umfasst:
    Erzeugen einer erweiterten Menge von Quantisierungsebenen,
    Beschneiden der Menge von Quantisierungsebenen unter Verwendung einer TCVQ-Technik.
  19. Sprachcodierer auf der Basis von LPC-Techniken, der Mittel zum Quantisieren einer Menge von LPC-Filterkoeffizienten mittels Vektorquantisierung umfasst und ferner umfasst:
    Mittel zum Erzeugen einer erweiterten Menge von Quantisierungsebenen;
    Mittel zum Beschneiden der Menge von Quantisierungsebenen unter Verwendung einer TCQ-Technik.
EP94103204A 1993-03-03 1994-03-03 Verfahren und Vorrichtung zur Sprachkodierung mit Trellis-kodierter Quantisierung für LPC- Quantisierung Expired - Lifetime EP0614075B1 (de)

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JP3557255B2 (ja) * 1994-10-18 2004-08-25 松下電器産業株式会社 Ｌｓｐパラメータ復号化装置及び復号化方法
US6128346A (en) * 1998-04-14 2000-10-03 Motorola, Inc. Method and apparatus for quantizing a signal in a digital system
PL3125241T3 (pl) * 2014-03-28 2021-09-20 Samsung Electronics Co., Ltd. Sposób i urządzenie do kwantyzacji współczynnika predykcji liniowej oraz sposób i urządzenie do kwantyzacji odwrotnej
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US4975956A (en) * 1989-07-26 1990-12-04 Itt Corporation Low-bit-rate speech coder using LPC data reduction processing

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ITMI930406A1 (it) 1994-09-03
EP0614075A2 (de) 1994-09-07
EP0614075A3 (de) 1995-08-02
ITMI930406A0 (it) 1993-03-03
IT1271959B (it) 1997-06-10
DE69424960D1 (de) 2000-07-27

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