**5. Simulation and result**

The results are initially developed through MATLAB and then hardware implementation are achieved to verify the real-time implementation.

## **5.1 MATLAB**

Experiments are performed in MATLAB 2010a. The proposed algorithm uses classical/pop music and speech audio clips in order to evaluate the performance [20]. These are three different types of audio clips are considered as they have different characteristics, perceptual properties and energy distribution. These audio signal have various distinct characteristics and also contains some selective features such as low energy, pulse clarity, pitch (in Hz.), inharmonicity, sampling rate (in Hz.), zero crossing rate (per second), spectral irregularity, temporal length (seconds/sample), tempo (in bpm), rms energy, etc. Each audio sample is of mono file of a 16-bit with sampling rate of 44.1 kHz of WAVE format. The watermark is of binary image of a 30 30 bits as in **Figure 10**. The synchronization code is a 16-bit Barker code of value "1111100110101110". The wavelet is applied with two decomposition levels. Array size m is 50 and the range of quantization step size Δ starts from 0.15 for speech audio and goes up to 0.6 for pop audio signal. The performance of audio watermarking algorithms is quantified by robustness, payload and inaudibility parameter [21].

**Figure 10.** *Watermark.*

The inaudibility is measured using signal to noise ratio (SNR). It is a used to calculate the similarity between distorted watermarked audio signal and undistorted original audio signal. SNR is calculated as in Eq. (10):

$$SNR = -10\log\_{10}\left|\frac{\varepsilon\_l f\_l^2}{\left(\Sigma\_l(j\_l - f\_i)\right)^2}\right|\tag{7}$$

b.**Additive white Gaussian noise (AWGN)**: to evaluate performance, Gaussian noise is inserted in the watermarked signal till an SNR reaches to 20 db.

c. **Low-pass filtering**: Butterworth filter of second-order is used at 11,025 Hz

e. **MP3 compression 64 kbps**: the layer-3 compression of MPEG-1 is being applied. The audio signal with watermark is compressed with 64 kbps bit-rate

f. **MP3 compression 128 kbps**: the layer-3 compression of MPEG-1 is being applied. The audio signal with watermark is compressed with 128 kbps bit-rate

g.**Random cropping**: total sample of around 10% are cropped at randomly

h.**Invert**: all sample values are inverted in time domain with phase shift 180°.

i. **Echo addition**: an echo signal is added (with a decay of 41% and a delay of

j. **Denoising**: the audio signal with watermark is denoised with function of

k.**Pitch shifting**: it is most difficult attack for audio watermarking algorithms, because it tends to shift frequency fluctuation. In the results, the pitch is being shifted around one higher degree and one lower degree. These are applied to all

The general comparison is made between our proposed method and two similar

The architecture is designed and implemented using Verilog HDL and targeting vertex 5 xc5vlx20t-2ff323 FPGA. We synthesized on Xilinx ISE 14.7. Each input and

methods [23] and is given in **Table 2**. As per reported results in **Table 2**, our proposed algorithm has higher capacity of embedding and lower value of BER. The proposed algorithm may achieve higher performance by reducing payload (which would be achieved by decomposition level increase for wavelet transform or length of array increases). The strength for embedding would increase with that approach.

ð10Þ

"automatic click remover" available in Adobe Audition 3.0.

is re-sampled at 22.05 kHz, and again sampled back at 44.1 kHz.

*Hardware Implementation of Audio Watermarking Based on DWT Transform*

and subsequently decompressed in the WAVE format.

and subsequently decompressed in the WAVE format.

selected positions (front, middle and back).

98 ms) inside the watermarked audio signal.

three audio signals are shown in given in **Table 1**.

The data payload is considered as 220 bps.

**5.2 Comparison with related work**

**5.3 Hardware results**

**207**

The payload data of the proposed algorithm is shown as:

d.**Re-sampling**: the sampling rate of the watermark signal is 44.1 kHz, further it

cutoff frequency.

*DOI: http://dx.doi.org/10.5772/intechopen.86087*

where fi is original audio signal, whereas fi' is watermarked audio signal. It helps to calculate the noise induced in the watermark and defines the inaudibility.

**Robustness**: normalized correlation (NC) measure the similarity between original and extracted is given by:

$$NC(\hat{\mathbf{w}}, \mathbf{w}^{\hat{\mathbf{w}}}) = \frac{\Sigma\_{l=1}^{M} \Sigma\_{f=1}^{M} \mathbf{w}(l, j) \mathbf{w}^{\dagger}(l, j)}{\sqrt{\Sigma\_{l=1}^{M} \Sigma\_{f=1}^{M} \mathbf{w}^{\dagger}(l, j)} \sqrt{\Sigma\_{l=1}^{M} \Sigma\_{f=1}^{M} \mathbf{w}^{\dagger}(l, j)}} \tag{8}$$

here w is original watermark, w<sup>0</sup> defines the extracted watermark, and i and j are indices to represent the watermark image. Generally, NC is to be considered as equal to 1. The robustness performance is measured using bit error rate (BER) as in Eq. (9).

$$BER(\hat{\mathbf{w}}, \mathbf{W}^r) = \frac{\text{Number of error bits}}{\text{Number of total bits}} \times 100\% \tag{9}$$

The different attacks are considered for the robustness measurement of our proposed algorithm. The detailed of each signal processing attacks are defined and results are defined in **Table 1** [22].

a. **Re-quantization**: original watermarked audio signal of 16 bit/sample is down re-quantized at 8 bits/sample, which further back quantized to 16 bits/sample.


### **Table 1.**

*Experimental results for robustness of proposed algorithm.*


The payload data of the proposed algorithm is shown as:

$$B = \begin{matrix} R \\ m \times 2^k \end{matrix} \text{(ps)}\tag{10}$$

The data payload is considered as 220 bps.

### **5.2 Comparison with related work**

The general comparison is made between our proposed method and two similar methods [23] and is given in **Table 2**. As per reported results in **Table 2**, our proposed algorithm has higher capacity of embedding and lower value of BER. The proposed algorithm may achieve higher performance by reducing payload (which would be achieved by decomposition level increase for wavelet transform or length of array increases). The strength for embedding would increase with that approach.
