Classical Time Series Classification algorithms are dominated by feature engineering strategies. One of the most prominent of these transforms is ROCKET, which achieves strong performance through random kernel features. We introduce SPROCKET (Selected Prototype Random Convolutional Kernel Transform), which implements a new feature engineering strategy based on prototypes. On a majority of the UCR and UEA Time Series Classification archives, SPROCKET achieves performance comparable to existing convolutional algorithms and the new MR-HY-SP ( MultiROCKET-HYDRA-SPROCKET) ensemble's average accuracy ranking exceeds HYDRA-MR, the previous best convolutional ensemble's performance. These experimental results demonstrate that prototype-based feature transformation can enhance both accuracy and robustness in time series classification.

In a guaranteed application, In most cases, the lines are covered to comply with the robot course shifts past it, sliding into the safety regulations and avoid possible consequences. As track as it progresses. This obstacle can be addressed by a result of all considerations, pipe networks are clearly controlling the robot using potentially working transmission used to transport liquids and gases in stables and urban parts. MRINSPECT-VI [10, 11] uses multi-colossal areas.We also showed that bioactivated robots with caterpillars, transfer parts to control the speed of three modules. However, inchworms, walking parts [1] and screw-driven a central transmission system is used, in which he structures [2] are suitable for different needs. Anyway, distributes the work and speed to three modules. This perspective most of them use dynamic control techniques to guide and caused the focal yield (Z) to rotate faster than her move the line. The reliance on the robot's course in the other two results (X and Y), making the Z yield actually line added to the difficulty, and unless a common control affected by hatch unfolding. This is caused by the fact procedure was included, the robots were similarly slippery.

This paper proposes a voice conversion (VC) method based on a sequence-to-sequence (S2S) learning framework, which enables simultaneous conversion of the voice characteristics, pitch contour, and duration of input speech. We previously proposed an S2S-based VC method using a transformer network architecture called the voice transformer network (VTN). The original VTN was designed to learn only a mapping of speech feature sequences from one speaker to another. The main idea we propose is an extension of the original VTN that can simultaneously learn mappings among multiple speakers. This extension called the many-to-many VTN makes it able to fully use available training data collected from multiple speakers by capturing common latent features that can be shared across different speakers. It also allows us to introduce a training loss called the identity mapping loss to ensure that the input feature sequence will remain unchanged when the source and target speaker indices are the same. Using this particular loss for model training has been found to be extremely effective in improving the performance of the model at test time. We conducted speaker identity conversion experiments and found that our model obtained higher sound quality and speaker similarity than baseline methods. We also found that our model, with a slight modification to its architecture, could handle any-to-many conversion tasks reasonably well.