Abstract: Methods and systems are disclosed configured to perform automated volume leveling on speech content in an audio file containing speech and non-speech segments. A low pass filter and a high pass filter may be applied to the audio data, and normalization may be performed. Speech and non-speech segments may be detected. Gain adjustments may be made to achieve a substantially constant short term loudness. Processing may be applied to enhance speech parameters, such as attack and release. An upward expander may be used to achieve a target loudness level. A limited and/or dynamic range compressor may be utilized to satisfy true peak and/or short term loudness specifications. A file of processed audio data may be generated and transmitted to one or more destinations for broadcast and/or streaming.

Abstract: Systems and methods are configured to process audio signals to identify content-types. Audio content is received at an audio decoder which decodes the audio content. The decoded audio content is segmented into frames by applying a windowing function to a given audio frame using a window having a time width related to a delay time of the decoder. A power spectrum estimate of a given frame is determined. A mel filter bank is applied to the power spectrum of the frame. A DCT matrix is applied to filter bank energies to generate a DCT output. A log of the DCT output is used to generate a mel coefficient 1. A threshold for the content is dynamically determined. The mel coefficient 1 and the dynamically determined threshold are used to detect a near silence between content-types and to identify the content-types.

Abstract: Systems and methods are configured to process audio signals to identify content-types. Audio content is received at an audio decoder which decodes the audio content. The decoded audio content is segmented into frames by applying a windowing function to a given audio frame using a window having a time width related to a delay time of the decoder. A power spectrum estimate of a given frame is determined. A mel filter bank is applied to the power spectrum of the frame. A DCT matrix is applied to filter bank energies to generate a DCT output. A log of the DCT output is used to generate a mel coefficient 1. A threshold for the content is dynamically determined. The mel coefficient 1 and the dynamically determined threshold are used to detect a near silence between content-types and to identify the content-types.

Abstract: System and methods are provided configured to reveal differences in content, such as commercial and program content. An input port may be configured to receive a first plurality of bit stream audio channels. A circuit may optionally be configured to enhance artifacting, noise, and/or bit rate errors and may optionally reduce redundant signal information. A signal processor circuit may be configured to transform the first plurality of bit stream audio channels into a frequency domain. A spectrum analysis system may be configured to compare properties of the first plurality of bit stream audio channels to one or more reference signals. An identification circuit may be configured to identify different content types included in the first plurality of bit stream audio channels.

Abstract: System and methods are provided configured to reveal differences in content, such as commercial and program content. An input port may be configured to receive a first plurality of bit stream audio channels. A circuit may optionally be configured to enhance artifacting, noise, and/or bit rate errors and may optionally reduce redundant signal information. A signal processor circuit may be configured to transform the first plurality of bit stream audio channels into a frequency domain. A spectrum analysis system may be configured to compare properties of the first plurality of bit stream audio channels to one or more reference signals. An identification circuit may be configured to identify different content types included in the first plurality of bit stream audio channels.