A sound signal processing method according to an embodiment includes a step of acquiring an input sound signal, a step of acquiring a beat number per unit time period from the input sound signal, a step of normalizing the input sound signal with the beat number per unit time period, a step of calculating a beat spectrum of the normalized input sound signal, and a step of calculating a rhythm similarity between the beat spectrum of the normalized input sound signal and a normalized beat spectrum calculated from a reference sound signal.

Captured vocals may be automatically transformed using advanced digital signal processing techniques that provide captivating applications, and even purpose-built devices, in which mere novice user-musicians may generate, audibly render and share musical performances. In some cases, the automated transformations allow spoken vocals to be segmented, arranged, temporally aligned with a target rhythm, meter or accompanying backing tracks and pitch corrected in accord with a score or note sequence. Speech-to-song music applications are one such example. In some cases, spoken vocals may be transformed in accord with musical genres such as rap using automated segmentation and temporal alignment techniques, often without pitch correction. Such applications, which may employ different signal processing and different automated transformations, may nonetheless be understood as speech-to-rap variations on the theme.

Systems and methods are provided herein relating to audio matching. Interest points that are onsets are generally very efficient in audio matching in that they are robust to multiple types of distortion. Prominent onsets can be detected within an audio signal excerpt as interest points and combined as a function of a set of interest points to form a descriptor. Descriptors associated with an audio signal excerpt that contain a set of prominent onsets as interest points can be used in matching the audio signal excerpt to an audio reference. The benefits in generating and using prominent onsets within descriptors improve the accuracy of an audio matching system.

A device is provided as part of a system, the device being for capturing vibrations produced by an object such as a musical instrument. Via a fixation element, the device is fixed to a drum. The device has a sensor spaced apart from a surface of the drum, located relative to the drum, and a magnet adjacent the sensor. The fixation element transmits vibrations from its fixation point on the drum to the magnet. Vibrations from the surface of the drum and from the magnet are transmitted to the sensor. A method may further be provided for interpreting an audio input, such as the output of the sensors within the system, the method comprising identifying an audio event or grouping of audio events within audio data, generating a model of the audio event that includes a representation of a timbre characteristic, and comparing that representation to expected representations.

Provided are an audio file re-recording method and device, and a storage medium. The method includes: determining first time, the first time being start time of a recorded clip to be re-recorded in an audio file; playing a first recorded clip that has been recorded, the first recorded clip using the first time as end time in the audio file; upon arrival of the first time, collecting first voice data of a user to obtain a second recorded clip; and processing the first recorded clip and the second recorded clip to obtain a re-recorded audio file.

An application that manipulates audio (or audiovisual) content, automated music creation technologies may be employed to generate new musical content using digital signal processing software hosted on handheld and/or server (or cloud-based) compute platforms to intelligently process and combine a set of audio content captured and submitted by users of modern mobile phones or other handheld compute platforms. The user-submitted recordings may contain speech, singing, musical instruments, or a wide variety of other sound sources, and the recordings may optionally be preprocessed by the handheld devices prior to submission.

Real-time pitch detection of voiced musical notes involves converting sound waves, produced by a voiced rendition of one or more musical notes, to a time domain electronic audio signal. The electronic audio signal is processed to determine a true pitch of the time domain electronic audio signal. True pitch information is displayed in real-time, concurrent with the voiced rendition of each musical note. A pitch indicator conveys to a user information concerning the true pitch which has been determined. The true pitch is determined by segmenting the electronic audio signal into a plurality of audio signal samples and applying a constant-Q transform. Additional processing steps are applied to reduce pitch detection errors.

A keyboard musical instrument includes a string-striking mechanism (hammer), driver, sound receiver, analyzer, and adjuster. The hammer strikes a string responsive to a change in position of an associated key of keyboard. The driver drives the hammer under a driving condition in accordance with control data. The sound receiver generates an audio signal corresponding to a sound occurring in the vicinity of the hammer. The analyzer detects the hammer striking a string by analyzing the audio signal generated when the hammer operates. The adjuster adjusts the control data in accordance with results of the analysis. The analyzer detects hammer striking a string in accordance with an intensity of the audio signal occurring within a search range, which has a predetermined relationship along a time axis with regard to a time at which the hammer commences operation.

Collaboratively creating musical harmonies includes receiving a user selection of a particular harmony. In response to this selection, there is displayed on a display screen of a computing device a plurality of musical note indicators or notes to specify a first harmony part of a musical piece to be performed. Real-time pitch detection is used to determine a pitch of each note which is voiced by a person, and a graphic indication of the actual pitch which is sung is displayed in conjunction with the musical note indicators.

Captured vocals may be automatically transformed using advanced digital signal processing techniques that provide captivating applications, and even purpose-built devices, in which mere novice user-musicians may generate, audibly render and share musical performances. In some cases, the automated transformations allow spoken vocals to be segmented, arranged, temporally aligned with a target rhythm, meter or accompanying backing tracks and pitch corrected in accord with a score or note sequence. Speech-to-song music applications are one such example. In some cases, spoken vocals may be transformed in accord with musical genres such as rap using automated segmentation and temporal alignment techniques, often without pitch correction. Such applications, which may employ different signal processing and different automated transformations, may nonetheless be understood as speech-to-rap variations on the theme.