Bilateral Waveform Similarity Overlap Add Approach based on Time Scale Modification Principle for Packet Loss Concealment of Speech Signals

Bilateral Waveform Similarity Overlap Add Approach based on Time Scale Modification Principle for Packet Loss Concealment of Speech Signals

Miss. Rohini D. Patil Research Student, Department of Electronics,

DKTEs Textile and Engineering Institute, Ichalkaranji, Maharashtra

Prof (Mrs.) D. Y. Loni Assistant Professor, Department of Electronics,

DKTEs Textile and Engineering Institute, Ichalkaranji, Maharashtra

Abstract Packet arrival delays at receiver and packet loss in the network greatly affect the quality of received signals in Voice over IP (VoIP) networks. To reduce this quality degradation caused by packet loss, Packet Loss Concealment (PLC) techniques are used. In this paper we presented Bilateral Waveform Similarity Overlap Add approach (BWSOLA) using Time Scale Modification (TSM). BWSOLA is receiver based PLC algorithm, which uses both previous and afterward packets for concealment of lost packets. BWSOLA maintains consistency in amplitude, frequency and phase between concealed and adjacent speech signals. The paper includes detailed comparison of Waveform Similarity Overlap Add (WSOLA), Gain controlled Waveform Similarity Overlap Add (GWSOLA) and BWSOLA techniques. Results obtained clearly indicate the quality improvement in BWSOLA as compared to WSOLA and GWSOLA techniques used for concealment of lost packet. The parameters used for comparison are Euclidean, Manhattan and Chebychev distance.

Keywords VoIP; PLC; WSOLA; GWSOLA; BWSOLA;

Euclidean distance; Manhattan distance; Chebychev distance.

1. INTRODUCTION

   Voice over IP (VoIP) permits the transport of voice over public internet. Internet gives various value added services by bringing together voice and data. Unfortunately, VoIP does not yet provide best quality of voice data over the VoIP network. So the quality of delivered data is an important issue while using VoIP for real time communication. For non-real time applications such as Telnet, FTP (File Transfer Protocol), email etc., VoIP can be used in an efficient way because TCP (Transmission Control Protocol) ensures reliable delivery of data. TCP is connection oriented protocol that uses best effort service. It integrates various retransmission policies based on feedback and timeout mechanism for reliable data transfer. But for real-time applications, TCP does not promise the required quality of data demanded by real-time applications [1].

   IP networks are essentially best-effort networks with variable delay and loss. Voice traffic in the network can tolerate some packet loss. But, if the packet loss rate is greater than 5%, it is considered harmful to the voice quality and a good concealment technique is required for

   reconstruction of the lost packets [2]. Phoneme is the unit of sound that distinguishes one word from other. The length of a phoneme is typically between 80 to 100 ms. In voice transmission, when the duration of the packet loss is greater than the length of a phoneme it can change the meaning of a word [3]. VoIP applications use UDP (User Datagram Protocol) as the transport layer protocol. The Real Time Protocol (RTP) and Real Time Control Protocol (RTCP) are used to provide additional functionality such as adding sequence numbers and timestamps [4].

   Packet loss on network is a major cause of audio signal loss. It is caused by the transmission impairments such as the excess of the transmission capacity and congestion and also may be caused in wireless channel due to noise, co- channel interference and fading [5]. Sound quality of the signal with lost packets can be improved by PLC. Packet Loss Concealment approaches are of two types, one is sender based and another is receiver based approach. Sender based approaches reduces the packet transmission failure while receiver based approaches focus on improving the quality of reconstructed speech signals [6].

   Time Scale Modification (TSM) can be efficiently used for PLC of audio signals. Time scaling is a process of changing the speed or duration of an audio without affecting its pitch. Time Scale modification of speech signals can be used for supporting hearing impaired school children. Comparisons of results obtained from Overlap and Add (OLA) and Synchronous overlap and add (SOLA) shows that SOLA give improved results than OLA [7].

   For non-uniform real-time speech stretching the algorithm uses combination of Synchronous overlap and add (SOLA) with vowels, consonants, and silence detector and the obtained results showed that the algorithm given high quality of stretched speech [8]. For high quality time- scale modifications of speech WSOLA can be used. The time scaling algorithm should produce an output that maintains maximal local similarity to original signal. SOLA algorithm does not maintain maximum local similarity, WSOLA technique was introduced which overcome the drawbacks of SOLA. WSOLA technique ensures sufficient signal continuity at segment joins that occurred in original

   signal [9]. Time scaling of correctly received packets over heterogeneous packet switched networks based on time- scale modification using WSOLA, reduces the error due to lost packets. Additional delay and complexity required for concealment was reduced by new parameter selection scheme. Using subjective listening tests, the results compared shows that overall sound quality is higher for proposed technique [10]. A new technique for improving quality of speech in voice over IP using Time Scale Modification using Global Local Search Time Scale Modification (GLS-TSM) can provide flexible delay cutoffs to late arrived packets and reduce additional buffering delay. TSM was performed on the packets already present in the playout buffer [11].

   A sinusoidal model of successfully received speech signal is used to recover lost frames using both extrapolation and interpolation techniques. Especially for music signals, use of interpolation improves results [12]. The standard WSOLA algorithm only considers phase information and lacks in amplitude control, which leads to significant mismatch between the original and reconstructed speech signal. This problem can be overcome, by introducing gain in standard WSOLA algorithm. Gain controlled Waveform Similarity Overlap and Add (GWSOLA) adjusts the level of audio signal to maintain audio signal level consistent. GWSOLA performs better than WSOLA [13]. In the proposed algorithm, gain is introduced into the standard WSOLA technique which helped to adjust the amplitude of the voice. Simulation results show that introduction of a gain into the standard WSOLA technique, improves the quality of the restored voice signal than that standard WSOLA technique [14].

   Incremental subspace learning along with non-negative matrix factorization can be used for recovery of lost speech segments [15]. Packet loss concealment problem can be solved using G.729. In the proposed work 50% loss is considered for concealment [16]. The receiver based PLC algorithm used adaptive thresholding for classification of packets and linear-prediction based packet loss concealment

   is performed. The algorithm enhances the quality of signal with low complexity and gives higher quality of voice [17]. Bilateral Waveform Similarity Overlap Add (BWSOLA) algorithm based on WSOLA uses the bilateral information for reconstruction of lost packet. As algorithm uses both sided information, the quality of the reconstructed speech signal of the BWSOLA is much better than WSOLA and GWSOLA. BWSOLA outperforms the traditional approaches especially in the long duration data loss [18].

2. FRAMEWORK OF BILATERAL WAVEFORM SIMILARITY OVERLAP ADD (BWSOLA)

   p>TSM can be used for packet loss concealment. The BWSOLA algorithm starts working after the packet loss is detected at the receiver end. When packet lost is detected, both the forward and afterward speech signals contents are extracted they are used to reconstruct the lost speech data. The GWSOLA algorithm is applied to both sided packets and the results are stored in L_GWSOLA and R_GWSOLA respectively. As BWSOLA adopts different approaches for voiced and unvoiced speech signal, both sided signals needs to be assessed for determining its state (voiced or unvoiced). Autocorrelation function (ACF) along with periodogram technique is used to decide the state of speech signal by computing its fundamental frequency. Fig 1 shows the block diagram of Bilateral Waveform Similarity Overlap Add approach. According to the state of both sided packets they are categorized into four different categories as: 1) BV (Both Voiced) where the both previous and afterward speech signals are voiced, 2) PV (Previous Voiced) where the previous signals is voiced and the afterward signals is unvoiced, 3) AV (Afterward Voiced) where the previous speech signals is unvoiced and the afterward signal is voiced 4) BU (Both Unvoiced) where both the previous and afterward signals are unvoiced. Finally depending upon the voicing state, different reconstruction strategies are used to reconstruct the lost speech signal using BWSOLA.

   Input data

   GWSOL A

   GL_WSOL A

   GR_WSOL A

   Voicing

   decision BV

   PV

   Choose

   methods

   AV

   Loss concealed signal

   BU

   Figure 1 Block Diagram of Bilateral Waveform Similarity Overlap Add (BWSOLA)

3. IMPLEMENTATION OF WAVEFORM SIMILARITY OVERLAP ADD (WSOLA) APPROACH

   Standard WSOLA algorithm is suitable for packet loss concealment in real time voice communications. When the receiver detects a packet loss in the received audio signal, the packets before the lost packet are extended using TSM, to cover the gap of the missing packet such that TSM must preserve the pitch frequency and timbre of speech signal [10].WSOLA algorithm operates in two steps as:

   1. Analysis Step 2. Synthesis Step

      Fig 2. (a) shows the operation of analysis step in which input signal X[n] have 2 packets before the lost packet. In

      Figure 3. Decision of search region

      In Fig 3, imin and imax are start point and end point of search region respectively. After deciding the search region, cross- correlation is computed according to Equation (3)

      y1

      analysis step, 3 frames Sks are extracted from the input signal, at time instant xks according to the maximal cross correlated point and then in analysis step frames are added

      Ci X (i j)X (xk 1 y j)

      j0

      (3)

      with less overlap in output at position yks as shown in Fig

   2. (b), and we got the output signal Y[n].

      Input signal X[n]

      Figure 2. (a) Analysis step WSOLA algorithm: extract Sk

      Correlation value Ci gives the best index at which the next frame should start i. e. xk-1, where i fall within the search region. Then according to the value of Ci extract the next analysis frame. The procedure is repeated to get the next analysis frames. At the end of analysis step we get the overlapping analysis frames. In synthesis step, the second and third analysis frames are shifted with y and 2*y respectively, retaining first analysis frame. Then the shifted synthesis frames are overlap added using Equation (4)

      L1

      Output signal Y[n]

      Y[n] w[n yk ] X [n yk xk ]

      k 0

      w[n] = flattopwin window

      (4)

      Figure 2. (b) Synthesis step WSOLA algorithm: overlap and add

      For implementation we have considered 8 kHz sampling frequency of the input signal. Length of the packet is 20 ms

      1. e. 160 samples per packet. In the analysis step, n=2 number of packets before lost packet are extracted and m=3 number of analysis frames Sks are formed, length of each

4. IMPLEMENTATION OF GAIN CONTROLLED WAVEFORM SIMILARITY OVERLAP ADD (GWSOLA) APPROACH

   The operation of analysis step of GWSOLA is same as WSOLA approach. But in synthesis step, gain of each frame is introduced along with respective frames and then overlap add operation is performed. Gain of each frame is calculated using Equation (5)

   L1

   analysis frame is L and is calculated using Equation (1)

   ml

   X yk nSk (n)

   gk n0

   (5)

   L (1)

   n

   L1

   Sk

   n0

   (n)2

   We extract the first analysis frame of length L=240, then computed analysis time shift x using Equation (2)

   gk = gain of kth frame

   x (m 1)l L

   2(n 1)

   (2)

   Then the overlap add is performed using Equation (6)

   m

   The synthesis time shift y is Calculated using y = L/2. We got x=40 and y=120 samples. The search region should be centered on x i.e. on 40th sample from the start of analysis frame. Length of search region is LS, it should not be less than one pitch period and should not be greater than a phoneme. Location of Search region for analysis frame is shown in Fig 3.

   Y (n) gk SK (n yk )

   k 0

   Y[n] = output of GWSOLA

   (6)

5. IMPLEMENTATION OF BILATERAL WAVEFORM SIMILARITY OVERLAP ADD (BWSOLA) APPROACH

   For implementation of BWSOLA algorithm, the input speech signals i. e. previous and afterward signals are extended using GWSOLA, and stored in L_GWSOLA and

   Where EV is the energy of the voiced speech, EUV is the energy of unvoiced speech. And n=0, 1 l-1

   The adjust factor is applied on the voiced signal using equation (9)

   R_GWSOLA respectively.

   As shown in Fig 4 packet 1 and 2 are extended using

   l 1

   Y (n) S ( j) A( j)

   j0

   (9)

   GWSOLA and stored in L_GWSOLA. Packet 4 and 5 are extended using GWSOLA and stored in R_GWSOLA. As different methods are used for voiced and unvoiced signals, initially we have to decide the state of speech signal. Depending upon voicing state further different reconstruction strategies are chosen from BV, PV or AV, and BU.

   Figure 4. Evaluation of L_WSOLA and R_WSOLA

   A. Methodology for BV

   If both the previous and afterward speech signals are voiced, only previous or afterward speech signal is used to reconstruct lost signal it may cause misalignment between reconstruction waveform and the original waveform. To avoid this problem both the previous and afterward signal are used. In process of reconstruction, the previous and afterward speech signals are extended using GWSOLA. In order to avoid problem of misalignment, the reconstruction waveform based on afterward speech waveform. Cross- correlation function is used on L_GWSOLA and R_GWSOLA, to find the start point of reconstructed waveform, which the most similar to the previous speech waveform. We found the maximum correlation point over the length of search region and got the point of most similarity p, which denotes the start point of the most similar waveform. The reconstruction waveform of lost packet is extracted using equation 7

   l

   Where S (j) is the voiced speech signal, A (j) is the adjust factor.

   C. Methodology for BU

   If both previous and afterward packets are unvoiced then l/2 i.e. 80 samples are extracted from the end of the previous packet and l/2+10 i.e. 90 samples are extracted from start of afterward packet. Overlap add is performed on last 10 samples of previous and first 10 samples of afterward signal to get the output signal.

6. EXPERIMENTAL RESULTS AND DISCUSSION

   To implement and validate the algorithm, we have initially created input signal with lost packet. Input signal used is Speak up whatever you want, and loss of 54th packet was created. Then using the WSOLA, GWSOLA, BWSOLA approaches of TSM the lost packet is concealed and experimental results of these approaches are discussed in a, b, and c respectively. As the packet lossis manually generated, we used original lost packet to compare how close the reconstructed packet is with respect to the known lost packet.

   1. Experimental results and Discussion of WSOLA

      Fig 5 shows the results of WSOLA using only previous packets (L_WSOLA).From fig 5, we can see that the major deviation occures in the second half of the signal, as this approach uses only previous packets for TSM.

      RESULTS OF L-WSOLA FOR KNOWN LOST PACKET NO. 54

      0.05

      Concealed lost packet using L-WSOLA technique

      0.04 original known lost packet 0.03

      0.02

      0.01

      0

      -0.01

      YGR

      j p'

      ( j)

      (7)

      -0.02

      -0.03

      YGR is R_GWSOLA. The extracted reconstructed waveform is less than the packet length. To get the final reconstructed signal WSOLA is applied to the available reconstruction signal.

   2. Methodology for PV or AV

   When one sided speech signal is voiced, another is unvoiced, we extend voiced speech signal and adjust amplitude. The adjust factor is calculated using equation (8)

   -0.04

   -0.05

   0 20 40 60 80 100 120 140 160

   Figure 5. Comparison of the known lost packet and result of WSOLA approach using only previous packets (L_WSOLA)

   Fig 6 shows the results of WSOLA using only afterward packets (R_WSOLA). From fig 6, it can be seen that major deviation takes place in the first half of the signal, as this

   A(n) 1 n EV EUV

   EV (l 1)

   (8)

   approach uses only afterward packets for TSM.

   0.08

   0.06

   RESULTS of R-WSOLA FOR KNOWN LOST PACKET NO. 54

   Concealed lost packet using R-WSOLA technique original known lost packet

   0.15

   0.1

   RESULTS OF R-GWSOLA FOR KNOWN LOST PACKET NO. 54

   Concealed lost packet using R-GWSOLA technique original known lost packet

   0.05

   0.04

   0.02

   0

   0

   -0.05

   -0.1

   -0.02

   -0.15

   -0.04

   -0.2

   0 20 40 60 80 100 120 140 160

   -0.06

   0 20 40 60 80 100 120 140 160

   Figure 6. Comparison of the known lost packet and result of WSOLA approach using only afterward packets (R_WSOLA)

   From fig 5 and 6, it can be said that WSOLA algorithm the concealed packet deviates widely at the initial and tail parts of the packet.

   1. Experimental results and Discussion of GWSOLA

      The comparison of known lost packet and the result of PLC by using GWSOLA method using only previous packets are shown in Fig 7. The results are improved than WSOLA but deviation still exists.

      Figure 8. Comparison of the known lost packet and result of GWSOLA approach using only afterward packets (R_GWSOLA)

   2. Experimental results and Discussion of BWSOLA

      Fig 9, shows the comparison of known lost packet and the result of PLC by using BWSOLA algorithm. From Fig 9, we can see that deviation is reduced much more than WSOLA and GWSOLA. The concealed packet obtained using BWSOLA approach closely approximates the known lost packet compared to other approaches.

      RESULTS of BWSOLA FOR KNOWN LOST PACKET NO. 54

      0.05

      Concealed lost packet using BGWSOLA technique

      0.04 original known lost packet

      0.05

      0.04

      0.03

      0.02

      0.01

      0

      -0.01

      -0.02

      RESULTS of L-GWSOLA FOR KNOWN LOST PACKET NO. 54

      Concealed lost packet using L-GWSOLA technique original known lost packet

      0.03

      0.02

      0.01

      0

      -0.01

      -0.02

      -0.03

      -0.04

      0 20 40 60 80 100 120 140 160

      -0.03

      -0.04

      -0.05

      0 20 40 60 80 100 120 140 160

      Figure 9. Comparison of the known lost packet and result of BWSOLA approach (BWSOLA)

      Comparison of results is also performed by computing distance between the known lost packet and concealed packet using metrics like Euclidean distance, Manhattan

      Figure 7. Comparison of the known lost packet and result of GWSOLA

      approach using only previous packets (L_GWSOLA)

      Results of GWSOLA algorithm using only afterward packets are shown in fig 8. The comparison of known lost packet and the result of PLC shows that results are improved as compared to WSOLA but deviation still exist.

      Comparing fig 7 and 8, it can be observed that GWSOLA algorithm improves the results but deviation remains same in terms of amplitude levels in the known lost packet and result of GWSOLA.

      distance and Chebychev distance on three test signals. Three test signals used as

      1. Test signal-1 is You Have Mail.

      2. Test signal-2 is Good Bye.

      3. Test signal-3 is speak up whatever you want

   Test signal-1 has 27 packets; out of 27 packets we got 2 results better for LWSOLA i.e. 7.4%. Test signal-2 have 28 packets out of 28 we got 4 results better for LWSOLA i.e. 14.28%. Test signal-3 have 82 packets out of 82 we got 6 results better for LWSOLA i.e. 7.31%. Accordingly percentage is calculated for three test signal with all algorithms.

   	Test signal-1	Test signal-2	Test signal-3
   LWSOLA	7.4	14.28	7.31
   GLWSOLA	29.62	3.57	15.85
   RWSOLA	11.11	10.71	14.63
   GRWSOLA	7.4	28.57	23.17
   BWOLA	44.44	42.85	39.02

   	Test signal-1	Test signal-2	Test signal-3
   LWSOLA	7.4	14.28	7.31
   GLWSOLA	29.62	3.57	15.85
   RWSOLA	11.11	10.71	14.63
   GRWSOLA	7.4	28.57	23.17
   BWOLA	44.44	42.85	39.02

   Table I. Comparison WSOLA, GWSOLA and BWSOLA algorithms for 3 test signals using Euclidean Distance metric

   GWSOLA algorithm is used so amplitude and phase will also be matched as in original signal. From the results, we can conclude that BWSOLA algorithm gives better results than WSOLA and GWSOLA algorithms.

   REFERENCES

   By observing table I, we can see BWSOLA algorithm outperforms for all three test signals than WSOLA and GWSOLA algorithm.

   Table II. Comparison WSOLA, GWSOLA and BWSOLA algorithms using Manhattan Distance metric for 3 test signals

   	Test signal-1	Test signal-2	Test signal-3
   LWSOLA	7.4	10.71	8.53
   GLWSOLA	14.81	10.71	15.29
   RWSOLA	25.92	3.57	14.63
   GRWSOLA	14.81	32.14	17.07
   BWOLA	37.03	42.85	42.5

   From the comparison using Manhattan Distance in table II we can see that for all three test signals BWSOLA algorithm gives better results.

   Table III. Comparison WSOLA, GWSOLA and BWSOLA algorithms using Chebychev Distance metric for 3 test signals

   	Test signal-1	Test signal-2	Test signal-3
   LWSOLA	22.22	7.14	7.13
   GLWSOLA	14.81	10.71	21.95
   RWSOLA	7.4	25	13.41
   GRWSOLA	18.51	25	23.71
   BWOLA	37.03	32.14	32.92

   Table III gives comparison using Chebychev Distance after observing the results in table III, is can be said that WSOLA and GWSOLA gives very poor results as compared to BWSOLA.

7. CONCLUSION

The standard WSOLA algorithm is appropriate for packet loss concealment in real time voice communications. It extends the previous packets of the lost packet, such that the gap caused by the lost packet can be concealed by the stretched version of the previous packets, while the pitch frequency and timbre of the voice transmitted are maintained unchanged. But WSOLA lacks in efficient amplitude controls, which lead to significant mismatch between the original signal and the concealed signal. As in GWSOLA algorithm gain is introduced, the amplitude mismatch problem is effectively solved. But both the algorithms WSOLA and GWSOLA considers only previous and afterward packets i.e. only one side packets for concealment of lost packets. In BWSOLA algorithm both sided packets are used for concealment and also

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