We propose a novel pitch estimation technique called DeepF0, which leverages the available annotated data to directly learns from the raw audio in a data-driven manner. f0 estimation is important in various speech processing and music information retrieval applications. Existing deep learning models for pitch estimations have relatively limited learning capabilities due to their shallow receptive field. The proposed model addresses this issue by extending the receptive field of a network by introducing the dilated convolutional blocks into the network. The dilation factor increases the network receptive field exponentially without increasing the parameters of the model exponentially. To make the training process more efficient and faster, DeepF0 is augmented with residual blocks with residual connections. Our empirical evaluation demonstrates that the proposed model outperforms the baselines in terms of raw pitch accuracy and raw chroma accuracy even using 77.4% fewer network parameters. We also show that our model can capture reasonably well pitch estimation even under the various levels of accompaniment noise.
In today's scenario security of data is very big challenge in any communication. Numerous data security and hiding algorithms have been developed in the last decade. The Digital image Steganography is science of hiding sensitive information in another transmission medium to achieve secure and secret communication. In this paper we present the dual layer of security to the data, in which first layer is to encode data using Least Significant Bit image steganography method and in the second layer encrypt the data using Advance Encryption Standard Algorithm. Steganography does not replace the encryption of data, instead it provides extra security feature to it. In our work secret text message is hiding behind the digital image file and this image file is then encrypted using AES encryption algorithm.
A new approach to a surgical robotic platform for single incision laparoscopic or natural orifice transluminal endoscopic surgery is presented in this paper This platform allows insertion of up to four instruments including the robotic arms and the camera through a single cannula at the same footprint. After insertion of all instruments, a large central channel of 15 mm diameter is kept clear for the passage of additional laparoscopic instruments, such as passage or retrieval of suture needles, and/or suction irrigators which greatly facilitates the performance of complex surgical procedures. Phantom and animal trials have been performed to evaluate the insertion and retrieval sequences. These important features were made possible by internally-motorized robotic arms with 7 degrees of freedom and with no external mechanical device connections. The whole platform, together with the 3 degrees of freedom from the swivel system that support the cannula, has altogether 10 degrees of freedom to allow the operation of complex surgeries and access to all quadrants of the abdominal cavity. This new single-port robotic platform paves a new development direction for future non-invasive surgery.
Background and Objective: In smoking cessation clinical research and practice, objective validation of self-reported smoking status is crucial for ensuring the reliability of the primary outcome, that is, smoking abstinence. Speech signals convey important information about a speaker, such as age, gender, body size, emotional state, and health state. We investigated (1) if smoking could measurably alter voice features, (2) if smoking cessation could lead to changes in voice, and therefore (3) if the voice-based smoking status assessment has the potential to be used as an objective smoking cessation validation method.Methods: A systematic review of the scientific literature was conducted to compile studies on smoking status assessment based on voice features. We searched nine scientific databases for original studies involving the effects of smoking on voice features, the effects of smoking cessation on voice features.Results: A total of 34 studies were identified for review. We found that fundamental frequency, jitter, shimmer, harmonics to noise ratio, and other voice features are affected by smoking and could be used to assess smoking status.
Conclusion:Speech assessment of smoking status based on voice features has potential as a smoking status validation method, as it is simple, reliable, and less time-consuming. Furthermore, this study provides recommendations for future research on the objective speech assessment of smoking status based on voice features.
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