10 accepted papers
Speaker anonymization techniques are crucial for safeguarding user privacy in voice-based applications. However, these methods are susceptible to adversarial attacks that can compromise their effectiveness. This paper proposes attacker systems that leverage the power of fine-tuned TitaNet-Large and…
Singing voice separation (SVS) and speaker identification (SI) are two classic problems in speech signal processing. Deep neural networks (DNNs) solve these two problems by extracting effective representations of the target signal from the input mixture. Since essential features of a signal can be w…
Voice activity detection (VAD) based on deep learning has achieved remarkable success. However, when the traditional features (e.g., raw waveforms and MFCCs) are directly fed to the deep neural network model, the performance decreases because of noise interference. Here, we propose a robust VAD appr…
Audio watermarking has been developed to protect the copyright of audio signals. We considered the use of the distribution of the phase spectrum and propose an effective multi-bit audio watermarking method based on phase shifting. The proposed method is implemented on the basis of a frame-wise frame…
The method reported here realizes an inaudible echo-hiding based speech watermarking by using sparse subspace clustering (SSC). Speech signal is first analyzed with SSC to obtain its sparse and low-rank components. Watermarks are embedded as the echoes of the sparse component for robust extraction.…
The recent deep learning methods can offer state-of-the-art performance for Monaural Singing Voice Separation (MSVS). In these deep methods, the recurrent neural network (RNN) is widely employed. This work proposes a novel type of Deep RNN (DRNN), namely Proximal DRNN (P-DRNN) for MSVS, which improv…
Although humans are capable of using monaural and modulation cues for sound localization, it is not yet clear how they can use that information to estimate the direction of arrival (DOA) of a sound source in 3D space. Our previous study revealed that the head-related modulation transfer function (HR…
This paper proposes a watermarking method for speech signals based on Robust Principal Component Analysis (RPCA) and formant manipulations. As the spectrogram of speech has a relatively sparse structure, the core information of speech is extracted into a sparse matrix using RPCA so that formants can…
Many studies have investigated the relationship between the articulatory and auditory features for isolated speech sound and vowels. For fully understanding the mechanisms of speech production and perception, it is necessary to investigate the consonants in the same way. For this reason, in this stu…
This paper proposes a novel robust audio watermarking method based on phase coding. The quantization index modulation technique is employed for embedding watermarks into the phase spectrum of audio signals. To increase robustness of the proposed method, the region of phase spectrum that is resistant…