An audio signal processing apparatus is provided. The audio signal processing apparatus includes an input terminal receiving an input audio signal, a processor obtaining a difference between a playback loudness of the input audio signal and a desired loudness thereof and correcting a frequency band spectrum of an output audio signal for each of a plurality of frequency bands based on the difference between the playback loudness and the desired loudness of the input audio signal and a relationship between a loudness and a sound pressure for each of the plurality of frequency bands, and an output terminal outputting the output audio signal. The playback loudness is a loudness of the output audio signal when the input audio signal is output without the correction.

Some embodiments of the invention are directed to enabling a user to easily identify the frequency range(s) at which sound masking occurs, and addressing the masking, if desired. In this respect, the extent to which a first stem is masked by one or more second stems in a frequency range may depend not only on the absolute value of the energy of the second stem(s) in the frequency range, but also on the relative energy of the first stem with respect to the second stem(s) in the frequency range. Accordingly, some embodiments are directed to modeling sound masking as a function of the energy of the stem being masked and of the relative energy of the masked stem with respect to the masking stem(s) in the frequency range, such as by modeling sound masking as loudness loss, a value indicative of the reduction in loudness of a stem of interest caused by the presence of one or more other stems in a frequency range.

A variable-resolution graphic equalizer providing an improved interface for controlling gain values across the entire audio spectrum using many narrow-band filters (e.g., 120). It allows user selection of a frequency range for graphic equalization and automatically maps a reduced and fixed number of sliders to the selected range based on the number of filter bands falling within the selected range. In an audio processing system, specific user interface regions are highlighted to display selected frequency ranges and corresponding selected sliders to allow for rapid and precise equalization of the full audio spectrum using the many narrow-band filters.

A sound processing system operative to measure the level of diffusivity of the sounds present in the input sound signal. The system includes a plurality of input channels for receiving audio signals from an audio scene, the audio scene comprising at least one target sound in the presence of background noise. A diffusivity measurement unit is included so as to be operably coupled to the plurality of input channels to receive the audio signals therefrom and measure a level of diffusivity of the sounds present therein. A leveler unit is operably coupled to the plurality of input channels for receiving the audio signals therefrom and for applying a gain to the audio signals to minimize variations in the audio signal levels. A controller is operably coupled to the diffusivity measurement unit and the leveler unit to control the gain applied to the audio signals by the leveler unit based on the level of diffusivity of the sounds present therein.

An adjusting system and an adjusting method for equalization processing are provided. Frequency band energies of sound receiving signals are obtained. The frequency band energies correspond to different frequency bands, respectively. Target gains corresponding to frequency bands are determined according to the frequency band energies. Frequency responses of filtering processing with respect to a plurality of center frequencies are obtained. Equalization gains corresponding to the frequency bands and having the least gain error are determined. The gain error is related to a difference between the amplitude obtained after the equalization gains are reflected on the frequency responses corresponding to the filtering processing and the target gains. The equalization gains are inputted into the filtering processing according to the corresponding frequency bands. Accordingly, the impact of the filtering processing can be reduced.

An electronic device and an equalizer adjustment method thereof for adjusting gain settings of an equalizer according to the volume of the output signal are disclosed. The method includes the steps of: setting a volume gain value table including a plurality of volume values through a gain value setting module, which are a first volume value to an Nth volume value with volume incrementally increasing, each of the plurality of volume values including a set of correction parameters which including a plurality of compensation gain values corresponding to a plurality of target frequencies, respectively; storing the volume gain value table in a storage module; obtaining a volume of the output signal; loading the volume gain value table according to the volume of the output signal to obtain the corresponding set of correction parameters; and adjusting gain value settings of an equalizer for different frequencies of sound.

Systems, methods, and apparatus are provided for equalization preference learning for digital audio modification. A method for listener calibration of an audio signal includes modifying a reference sound using at least one equalization curve; playing the modified reference sound for a listener; accepting listener feedback regarding the modified reference sound; and generating a weighting function based on listener feedback. A listener audio configuration system includes an output providing a sound for listener review; an interface accepting listener feedback regarding the sound; and a processor programming an audio device based on listener feedback.

Certain embodiments provide methods and systems for managing a sound profile. An example playback device includes a network interface and a non-transitory computer readable storage medium having stored therein instructions executable by the processor. When executed by the processor, the instructions are to configure the playback device to receive, via the network interface over a local area network (LAN) from a controller device, an instruction. The example playback device is to obtain, based on the instruction, via the network interface from a location outside of the LAN, data comprising a sound profile. The example playback device is to update one or more parameters at the playback device based on the sound profile. The example playback device is to play back an audio signal according to the sound profile.

The present disclosure discloses a sound control device for controlling load based on continuous sound control signal, which comprises a sound control component, a sound control processing component, a CPU processing device, an execution port component and a load, wherein the sound control component is connected to the sound control processing component; the sound control component collects a sound control signal and transmits the sound control signal to the sound control processing component; the sound control processing component is connected to the CPU processing device; and the CPU processing device is respectively connected with the execution port component and the load.

A deep learning method-based tonal balancing method, apparatus, and system, the method includes: extracting features from audio data to obtain audio data features, generating audio balancing results by using a trained audio balancing model based on the obtained audio data features. The present invention employs deep neural networks and unsupervised deep learning method to solve the problems of audio balancing of unlabeled music and music of unknown style. The present invention also combines user preferences statistics to achieve a more rational multi-style audio balancing design to meet individual needs.