Systems and methods for adjusting pitch of an audio signal include detecting input notes in the audio signal, mapping the input notes to corresponding output notes, each output note having an associated upper note boundary and lower note boundary, and modifying at least one of the upper note boundary and the lower note boundary of at least one output note in response to previously received input notes. Pitch of the input notes may be shifted to match an associated pitch of corresponding output notes. Delay of the pitch shifting process may be dynamically adjusted based on detected stability of the input notes.

A multifunctional microphone, includes a controlling mainboard; a sound collector electrically connected with the controlling mainboard; a speaker electrically connected with the controlling mainboard; and a sound adjusting module arranged on the controlling mainboard and configured to adjust sound collected by the sound collector.

Disclosed herein are method, system, and computer program product embodiments for performing the continuous tuning of received audio input from an earpiece or microphone especially customized for karaoke singing, wherein the audio input may be mixed with user-selected song input, and the joint mixed input is independently altered in the frequency domain for output to an earpiece worn by a user as well as separately for an additional audio output to an external connected speaker, for an optimal karaoke experience.

Despite many practical limitations imposed by mobile device platforms and application execution environments, vocal musical performances may be captured and continuously pitch-corrected for mixing and rendering with backing tracks in ways that create compelling user experiences. Based on the techniques described herein, even mere amateurs are encouraged to share with friends and family or to collaborate and contribute vocal performances as part of virtual “glee clubs.” In some implementations, these interactions are facilitated through social network- and/or eMail-mediated sharing of performances and invitations to join in a group performance. Using uploaded vocals captured at clients such as a mobile device, a content server (or service) can mediate such virtual glee clubs by manipulating and mixing the uploaded vocal performances of multiple contributing vocalists.

Digital signal processing and machine learning techniques can be employed in a vocal capture and performance social network to computationally generate vocal pitch tracks from a collection of vocal performances captured against a common temporal baseline such as a backing track or an original performance by a popularizing artist. In this way, crowd-sourced pitch tracks may be generated and distributed for use in subsequent karaoke-style vocal audio captures or other applications. Large numbers of performances of a song can be used to generate a pitch track. Computationally determined pitch trackings from individual audio signal encodings of the crowd-sourced vocal performance set are aggregated and processed as an observation sequence of a trained Hidden Markov Model (HMM) or other statistical model to produce an output pitch track.

An input musical sound signal is input to band pass filters, and pass musical sound signals for each sound pitch are acquired. Total level ratios based on a total of levels of a sound pitch lower than those of the pass musical sound signals are calculated from the levels that are levels of the pass musical sound signals, and output coefficients are acquired on the basis of the total level ratios. Levels of octave musical sound signals acquired by converting the pass musical sound signals into sound pitches lower than those of the pass musical sound signals by one octave are multiplied by the output coefficients. In accordance with this, the octave musical sound signals of a low sound pitch can be extracted from among the octave musical sound signals.

Audio data of an original work is received. Text in the audio data is translated to a target language. The audio data is passed to a first deep learning model to learn voice features in the audio data. The audio data is passed to a second deep learning model to learn audio properties in the audio data. The translated text is synchronized to play in the position of original text of the original work in a synthesized voice. A translated audio data of the original work is created by combining the synchronized translated text in the synthesized voice with music of the audio data.

Disclosed is subject matter related generally to audio signal processing, and in particular to automatic pitch correction systems.

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.

A method of post-processing an audio recording in an audio production equipment (101) includes receiving at least one audio track (91) of the audio recording, analyzing one or more characteristics (80) of the at least one audio track (91) to identify a timing of one or more points of interest (251-254) of a content (201-203, 269) of the at least one audio track (91), and adding, to the audio recording and at the timing of the one or more points of interest (251-254), one or more audience reaction effects (261-264).