In this paper, we present a novel audio watermarking scheme based on spread spectrum techniques that embeds a digital watermark within an audio signal using the instantaneous mean frequency ( I W ) ofthe signal. Audio watermarking offers a solution to datapiracy and helps to protect the rights of the artists and copyright holders. Our content-based algorifhm aims to satbfy and mmimize both imperceptibilify and robustness ofthe watermark.In addition, our technique uses the short-time Fourier transform of the original audio signal to estimate a weighted IMF of the signal. Based on the masking properties of the psychoacoustic model, we then derive the required soundpressure level of the watermark. From these results, modulation is performed to produce a signal-dependent watermark that is imperceptible.The proposed method allows 25 bits to be embedded and recovered within a 5 second sample of an audio signal. Experimental results have shown that our scheme is robust to common signal processing attacks including filtering, MP3 compression and noise addition.
In this paper, a novel audio watermarking scheme based on spread spectrum techniques is proposed. This technique embeds a digital watermark within an audio signal using the instantaneous mean frequency (IMF) of the signal. Audio watermarking offers a solution to data piracy and helps to protect the rights of the artists and copyright holders. The proposed content-based algorithm aims to satisfy and maximize both imperceptibility and robustness of the watermark In addition, the technique uses the short-time Fourier transform of the original audio signal to estimate a weighted IMF of the signal. Based on the masking prop erties of the psychoacoustic model, the required sound pressure level of the watermark is calculated. Modulation is then performed to produce a signal dependent watermark that is imperceptible. The proposed method allows 25 bits to be embedded and recovered within a 5 s sample of an audio signal. Experi mental results have shown that the scheme is robust to common signal processing attacks including filtering, J\IlP3 compression, additive noise and resampling with a bit error rate in the range of 0-13%. Dans cet article, une nouvelle approche de filigrane audio basee sur des techniques d'etalement spectrales est proposee. Cette technique inclut un filigrane numerique ajoute au signal audio et employant la frequence moyenne instantanee (FJ'v1I) du signal. Le filigrane audio offre une solution au piratage de donnees et pennet la protection des droits d'auteur et d'artistes. L'algorithme propose est a la fois robuste tout en demeurant imperceptible. En plus, notre technique emploie une transformee de Fourier a temps reduit du signal audio pour estimer la FMI du signal. En se basant sur les proprietes masquantes du modele psycho acoustique, la pression sonore du filigrane audio est calculee. La modulation est ensuite ajoutee pour produire un filigrane imperceptible. La methode proposee permet d'inclure et de recuperer 25 bits, ceux-ci dans une trame sonore de 5 s. Les resultats experimentaux ont montre que l'approche est robuste face aux at taques communes de traitement de signal incluant Ie filtrage, la compression MP3, Ie bruit additif et Ie re-echantillonnage employant un taux d' erreur de bit de o a 13 %.
This research focuses on the application of joint time-frequency (TF) analysis for watermarking and classifying different audio signals. Time frequency analysis which originated in the 1930s has often been used to model the non-stationary behaviour of speech and audio signals. By taking into consideration the human auditory system which has many non-linear effects and its masking properties, we can extract efficient features from the TF domain to watermark or classify signals. This novel audio watermarking scheme is based on spread spectrum techniques and uses content-based analysis to detect the instananeous mean frequency (IMF) of the input signal. The watermark is embedded in this perceptually significant region such that it will resist attacks. Audio watermarking offers a solution to data privacy and helps to protect the rights of the artists and copyright holders. Using the IMF, we aim to keep the watermark imperceptible while maximizing its robustness. In this case, 25 bits are embedded and recovered witin a 5 s sample of an audio signal. This scheme has shown to be robust against various signal processing attacks including filtering, MP3 compression, additive moise and resampling with a bit error rate in the range of 0-13%. In addition content-based classification is performed using TF analysis to classify sounds into 6 music groups consisting of rock, classical, folk, jazz and pop. The features that are extracted include entropy, centroid, centroid ratio, bandwidth, silence ratio, energy ratio, frequency location of minimum and maximum energy. Using a database of 143 signals, a set of 10 time-frequncy features are extracted and an accuracy of classification of around 93.0% using regular linear discriminant analysis or 92.3% using leave one out method is achieved.
This research focuses on the application of joint time-frequency (TF) analysis for watermarking and classifying different audio signals. Time frequency analysis which originated in the 1930s has often been used to model the non-stationary behaviour of speech and audio signals. By taking into consideration the human auditory system which has many non-linear effects and its masking properties, we can extract efficient features from the TF domain to watermark or classify signals. This novel audio watermarking scheme is based on spread spectrum techniques and uses content-based analysis to detect the instananeous mean frequency (IMF) of the input signal. The watermark is embedded in this perceptually significant region such that it will resist attacks. Audio watermarking offers a solution to data privacy and helps to protect the rights of the artists and copyright holders. Using the IMF, we aim to keep the watermark imperceptible while maximizing its robustness. In this case, 25 bits are embedded and recovered witin a 5 s sample of an audio signal. This scheme has shown to be robust against various signal processing attacks including filtering, MP3 compression, additive moise and resampling with a bit error rate in the range of 0-13%. In addition content-based classification is performed using TF analysis to classify sounds into 6 music groups consisting of rock, classical, folk, jazz and pop. The features that are extracted include entropy, centroid, centroid ratio, bandwidth, silence ratio, energy ratio, frequency location of minimum and maximum energy. Using a database of 143 signals, a set of 10 time-frequncy features are extracted and an accuracy of classification of around 93.0% using regular linear discriminant analysis or 92.3% using leave one out method is achieved.
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