Abstract: The present invention provides IP4-4,6 substituted derivatives, their methods of synthesis and their uses. In some aspects, the IP4-4,6 derivative is a compound of formula I wherein R1, R3, R7, and R11 are OPO32?, and R5 and R9 are selected from the group consisting of —O(Alkyl)nX, —O(Alkyl)yCy(Alkyl2)y-Z, —O(Alkyl)yA(Alkyl)y-Z?, and their thiophosphate analogs. Also provided are methods, pharmaceutical compositions and formulations, methods of use, articles of manufacture, and kits for the treatment of diseases and conditions such as pathological crystallization-related diseases and conditions.

Abstract: The present disclosure relates to a method and system for performing packet loss concealment using a neural network system. The method comprises obtaining a representation of an incomplete audio signal, inputting the representation of the incomplete audio signal to an encoder neural network and outputting a latent representation of a predicted complete audio signal. The latent representation is input to a decoder neural network which outputs a representation of a predicted complete audio signal comprising a reconstruction of the original portion of the complete audio signal, wherein said encoder neural network and said decoder neural network have been trained with an adversarial neural network.

Abstract: A deep-learning-based system for performing automated multitrack mixing based on a plurality of input audio tracks is described herein. The system comprises one or more instances of a deep-learning-based first network and one or more instances of a deep-learning-based second network. Particularly, the first network is configured to, based on the 5 input audio tracks, generate parameters for use in the automated multitrack mixing. The second network is configured to, based on the parameters, apply signal processing and at least one mixing gain to the input audio tracks, for generating an output mix of the audio tracks.

Abstract: Described is a method of training a neural-network-based system for determining an indication of an audio quality of an audio input. The method includes obtaining, as input, at least one training set comprising audio samples. The audio samples include audio samples of a first type and audio samples of a second type, wherein each of the first type of audio samples is labelled with information indicative of a respective predetermined audio quality metric, and wherein each of the second type of audio samples is labelled with information indicative of a respective audio quality metric relative to that of a reference audio sample. The method further includes: inputting the training set to the neural-network-based system; and iteratively training the system to predict the respective label information of the audio samples in the training set.

Abstract: A method for calculating measures of similarity between time signals, which includes: acquiring and comparing data (xi, yj) of time signals (X, Y); assigning a one or a zero to every two compared data (xi, yj), depending on the result of said comparison, creating a data set; determining time sequences with said ones and zeros of the data set, each one being formed by consecutive sub-sequences of ones, separated by discontinuities of zeros; selecting the highest result of accumulated results obtained for each sub-sequence, adding for each determined point i, j of value one said one to the accumulated result of maximum value, from among the accumulated results at a point i?1, j?1 of said sub-sequence, a point i?2, j?1 of a sub-sequence of a second time sequence, and a point i?1, j?2 of a sub-sequence of a third sequence.

Abstract: A method for calculating measures of similarity between time signals, which includes: acquiring and comparing data (xi, yj) of time signals (X, Y); assigning a one or a zero to every two compared data (xi, yj), depending on the result of said comparison, creating a data set; determining time sequences with said ones and zeros of the data set, each one being formed by consecutive sub-sequences of ones, separated by discontinuities of zeros; selecting the highest result of accumulated results obtained for each sub-sequence, adding for each determined point i, j of value one said one to the accumulated result of maximum value, from among the accumulated results at a point i?1, j?1 of said sub-sequence, a point i?2, j?1 of a sub-sequence of a second time sequence, and a point i?1, j?2 of a sub-sequence of a third sequence.

Abstract: Systems and methods for determining similarity between two or more audio pieces are disclosed. An illustrative method for determining musical similarities includes extracting one or more descriptors from each audio piece, generating a vector for each of the audio pieces, extracting one or more audio features from each of the audio pieces, calculating values for each audio feature, calculating a distance between a vector containing the normalized values and the vectors containing the audio pieces, and outputting a response to a user or process indicating the similarity between the audio pieces. The descriptors can be used in performing content-based audio classification and for determining similarities between music. The descriptors that can be extracted from each audio piece can include tonal descriptors, dissonance descriptors, rhythm descriptors, and spatial descriptors.