Abstract: Techniques are described herein for tracking and analyzing musical activity (e.g., musical performances) captured by a music controller device. Data indicative of a musical performance by an individual is received. Metadata characterizing the musical data is generated. One or more analytics of the musical data is generated based, at least in part, on the metadata.

Abstract: A system providing a music service platform includes a music controller device, a client device, and a server. The music controller device receives musical data indicative of a music performance by an individual and generates metadata characterizing the musical data. The client device includes a software application that receives the musical data and the metadata from the music controller device and presents a graphical user interface for display on the client device. The graphical user interface includes information related to the musical data and the metadata. The server receives the musical data and the metadata, generates one or more analytics associated with the musical data, and performs one or more data aggregation techniques on the musical data.

Abstract: Techniques are described herein for generating a music fingerprint representative of a performance style of an individual. One or more characteristics associated with musical data are identified. A score to associated with each of the identified one or more characteristics is determined. The music fingerprint is generated based on the determine score for each of the identified one or more characteristics.

Abstract: Techniques are described herein for generating a music fingerprint representative of a performance style of an individual. One or more characteristics associated with musical data are identified. A score to associated with each of the identified one or more characteristics is determined. The music fingerprint is generated based on the determine score for each of the identified one or more characteristics.

Abstract: Techniques are described herein for tracking and analyzing musical activity (e.g., musical performances) captured by a music controller device. Data indicative of a musical performance by an individual is received. Metadata characterizing the musical data is generated. One or more analytics of the musical data is generated based, at least in part, on the metadata.

Abstract: A system providing a music service platform includes a music controller device, a client device, and a server. The music controller device receives musical data indicative of a music performance by an individual and generates metadata characterizing the musical data. The client device includes a software application that receives the musical data and the metadata from the music controller device and presents a graphical user interface for display on the client device. The graphical user interface includes information related to the musical data and the metadata. The server receives the musical data and the metadata, generates one or more analytics associated with the musical data, and performs one or more data aggregation techniques on the musical data.

Abstract: A system for detecting objects in a zone proximate to a machine includes a detection sensor, an output device, and a controller. The controller is configured to define an obstruction zone proximate to the machine and within the zone, receive detection signals from the detection sensor, determine if the detection signals indicate that an object exists within the obstruction zone, and determine if the object is indicative of a machine component, if the detection signals indicate that the object exists within the obstruction zone, based on a learned obstruction detection process. The learned obstruction detection process is configured to determine if the object is indicative of a machine component by comparing the detection signals with component-associated detection data. The controller is configured to provide an alert signal to the output device if the first object is not indicative a machine component.

Abstract: A system for detecting objects in a zone proximate to a machine includes a detection sensor, an output device, and a controller. The controller is configured to define an obstruction zone proximate to the machine and within the zone, receive detection signals from the detection sensor, determine if the detection signals indicate that an object exists within the obstruction zone, and determine if the object is indicative of a machine component, if the detection signals indicate that the object exists within the obstruction zone, based on a learned obstruction detection process. The learned obstruction detection process is configured to determine if the object is indicative of a machine component by comparing the detection signals with component-associated detection data. The controller is configured to provide an alert signal to the output device if the first object is not indicative a machine component.