An equalizer and a method of controlling same are provided. The equalizer includes a memory storing an EQ value set for a plurality of music attributes and storing a general-purpose EQ value; and a processor configured to: obtain an input music signal; calculate a plurality of probability values for the plurality of music attributes by analyzing attributes of the input music signal based on a convolutional neural network; calculate a moderate index between the plurality of probability values; generate an EQ value based on the plurality of probability values and the moderate index; and perform equalizing by applying the generated EQ value to the input music signal.

A leveling equalizer includes a graphic equalizer circuit, a first multiplication circuit, a second multiplication circuit, an addition circuit, and a gain control circuit. The graphic equalizer circuit processes a first input signal and output a first output signal and a second output signal. The first multiplication circuit multiplies the first output signal and one of an adjustable gain value and a fixed gain value to generate a first adjusted output signal. The second multiplication circuit multiplies the second output signal and another of the adjustable gain value and the fixed gain value to generate a second adjusted output signal. The addition circuit combines the first adjusted output signal and the second adjusted output signal to generate an equalizer output signal. The gain control circuit dynamically adjusts the adjustable gain value according to the equalizer output signal.

A system and method for providing a dynamic audio environment within a vehicle that includes receiving data associated with at least one audio stream that is being played through an audio system of the vehicle. The system and method also include determining a selected vehicle mode of the vehicle. The system and method additionally include selecting at least one audio effect that is associated with the selected vehicle mode of the vehicle to alter the at least one audio stream. The system and method further include controlling at least one audio source of the vehicle to provide the at least one audio stream according to the at least one audio effect.

Methods, apparatus, systems and articles of manufacture are disclosed methods and apparatus for audio equalization based on variant selection. An example apparatus includes a processor to obtain training data, the training data including a plurality of reference audio signals each associated with a variant of music and organize the training data into a plurality of entries based on the plurality of reference audio signals, a training model executor to execute a neural network model using the training data, and a model trainer to train the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.

Embodiments described herein provide for smart configuration of audio settings for a playback device. According to an embodiment, while a playback device is a part of a first zone group that includes the playback device and at least one first playback device, the playback device applies a first audio setting. The embodiment also includes the playback device joining a second zone group that includes the playback device and at least one second playback device. The embodiment further includes the playback device applying a second audio setting based on an audio content profile corresponding to the second zone group.

The present disclosure provides an adaptive equalization method and system for an audio system. The method includes: detecting an input signal and estimating signal energy of the input signal; determining an audio signal status based on the estimated signal energy; performing dynamic gain calculation based on the estimated signal energy and the audio signal status; and using the calculated gain to adjust equalization parameters, thereby performing dynamic equalization processing on the input signal.

In some embodiments, a method for performing at least one of enhancement, decoding, or rendering of a multichannel audio signal in response to compression feedback or feedback from a smart amplifier. For example, the compression feedback may be indicative of amount of compression applied to each of multiple frequency bands, of the audio signal or an enhanced audio signal generated in response thereto. The enhancement (e.g., bass enhancement) may include dynamic routing of audio content of the input audio signal between channels of an enhanced audio signal generated in response thereto. The enhancement and compression may be performed on a per speaker class basis. Other aspects are systems (e.g., programmed processors) and devices (e.g., devices having physically-limited bass reproduction capabilities, such as, for example, a notebook or laptop computer, tablet, soundbar, mobile phone, or other device with small speakers) configured to perform any embodiment of the method.

An amplifier stage uses a loaded transistor amplifier circuit including a load that causes greater second order harmonic distortion energy than third order harmonic distortion energy to be produced in said loaded transistor amplifier circuit for amplifying a source audio signal to produce an audio output signal. The spectrum of the fundamental orders of harmonic distortion is adjusted to improve perceived sound quality or listening enjoyment.

In accordance with some embodiments, systems, methods and media for automatically determining audio gain profiles for fitting personal audio output devices are provided. In some embodiments, a method for automatically determining audio gain profiles comprises: determining hearing thresholds; determining gain profiles based on the hearing thresholds generating augmented audio signals using the gain profiles; causing the augmented audio signals to be presented using a speaker of an output device; receiving a rating associated with each gain profile; determining fitness values based on the ratings; generating a new generation of gain profiles based on the fitness values, each gain profile comprising bits representing the set of gain values; augmenting the new generation of gain profiles by exchanging a string of bits from a first gain profile with a corresponding string of bits from a second gain profile; and outputting a final gain profile to the output device.

Methods, apparatus, systems and articles of manufacture are disclosed methods and apparatus for audio equalization based on variant selection. An example apparatus includes a processor to obtain training data, the training data including a plurality of reference audio signals each associated with a variant of music and organize the training data into a plurality of entries based on the plurality of reference audio signals, a training model executor to execute a neural network model using the training data, and a model trainer to train the neural network model by updating at least one weight corresponding to one of the entries in the training data when the neural network model does not satisfy a training threshold.