The present disclosure describes techniques for identifying music attributes. The described techniques comprises receiving audio data of a piece of music; determining at least one attribute of the piece of music based on the audio data of the piece of music using a model; the model comprising a convolutional neural network and a transformer; the model being pre-trained using training data, wherein the training data comprise labelled data associated with a first plurality of music samples and unlabelled data associated with a second plurality of music samples, the labelled data comprise audio data of the first plurality of music samples and label information indicative of attributes of the first plurality of music samples, and the unlabelled data comprise audio data of the second plurality of music samples.

A system for platform-independent visualization of audio content, in particular audio tracks utilizing a central computer system in communication with user devices via a computer network. The central system utilizes various algorithms to identify spoken content from audio tracks and selects visual assets associated with the identified content. Thereafter, a visualized audio track is available for users to listen and view. Audio tracks, for example Podcasts, may be segmented into topical audio segments based upon themes or topics, with segments from disparate podcasts combined into a single listening experience, based upon certain criteria, e.g., topics, themes, keywords, and the like.

Systems, methods, architectures, mechanisms and apparatus for generating an audio segment playlist configured to provoke a physiological response in a listener in accordance with a desired outcome category, comprising: selecting, from a features database, a plurality of audio segments having features associated with both listener information and the desired outcome category; and ordering within the playlist at least a portion of the selected audio segments in accordance with at least one feature progression associated with the outcome category.

A method and system for teaching oneself to learn to play a string instrument and master it by analyzing ones real-time hand/finger movement/technique/form, enabling oneself to progress and correct ones own mistakes simultaneously. In one aspect, a system for learning to play a string instrument is provided that includes a simulation instrument that includes a plurality of strings, wherein at least one of the strings includes at least one-touch sensing sensor thereon in communication with at least one processor to receive a signal from the at least one touch-sensing sensor and determine therefrom when and where on the at least one string a user applies pressure to the at least one string.

Provided are an electronic percussion instrument and a hit detection method. An overhang portion constituting the outer edge part of an edge frame is positioned further radially outward than the outer edge of a bow frame. Consequently, when the overhang portion is hit, the vibration resulting from the hit is detected by an edge sensor attached to the edge frame. In this way, it is possible to detect whether the overhang portion (the edge portion of an electronic cymbal has been hit by means of vibration detection by the edge sensor instead of by means of pressure detection by a sheet sensor.

Various embodiments herein provide a method for managing an audio based on a spectrogram. The method includes generating, by a transmitter device, the spectrogram of the audio. The method includes identifying a first spectrogram corresponding to vocals in the audio and a second spectrogram corresponding to music in the audio from the spectrogram of the audio, and extracting a music feature from the second spectrogram. The method includes transmitting a signal comprising the first spectrogram, the second spectrogram, the music feature and the audio to a receiver device. The method includes determining, by the receiver device, whether an audio drop is occurring in the received signal based on a parameter associated with the received signal. The method includes generating the audio using the first spectrogram, the second spectrogram, the music feature, in response to determining that the audio drop is occurring in the received signal.

A portable digital audio system for a musician. The digital audio system includes an amplifier for processing an audio signal from a musical instrument or microphone electronically connected to the digital audio system and a speaker for playing a sound associated with the audio signal processed by the amplifier. The portable digital audio system also includes an audio control system providing operational control of the digital audio system and a primary housing for supporting the amplifier, the audio control system, and the speaker. Further, the digital audio system has a touch screen display in electronic communication with the audio control system and supported by the primary housing.

Musical catalog amplification services that leverage or deploy a computer-based musical composition system are described. The computer-based musical composition system employs algorithms and, optionally, artificial intelligence to generate new music based on analyses of existing music. The new music may be wholly distinctive from, or may include musical variations of, the existing music. Rights in the new music generated by the computer-based musical composition system are granted to the rights holder(s) of the existing music. In this way, the musical catalog(s) of the rights holder(s) is/are amplified to include additional music assets. The computer-based musical composition system may be tuned so that the new music sounds more like, or less like, the existing music of the rights holder(s). Revenues generated from the new music are shared between the musical catalog amplification service provider and the rights holder(s).

A mobile device responds in real time to media content presented on a media device, such as a television. The mobile device captures temporal fragments of audio-video content on its microphone, camera, or both and generates corresponding audio-video query fingerprints. The query fingerprints are transmitted to a search server located remotely or used with a search function on the mobile device for content search and identification. Audio features are extracted and audio signal global onset detection is used for input audio frame alignment. Additional audio feature signatures are generated from local audio frame onsets, audio frame frequency domain entropy, and maximum change in the spectral coefficients. Video frames are analyzed to find a television screen in the frames, and a detected active television quadrilateral is used to generate video fingerprints to be combined with audio fingerprints for more reliable content identification.

A method of generating audio assets, comprising the steps of: receiving an input audio asset having a first duration, generating an input image representative of the input audio asset, training a generative model on the input image and implementing the trained generative model to generate an output image representative of an output audio asset having a second duration different to the first duration, and generating the output audio asset based on the output image.