Abstract: A hearing prosthesis that automatically adjusts itself to a surrounding listening environment is provided. In one aspect, the automatic adjustment is achieved by controlling one or several algorithm parameters of a predetermined signal processing algorithm. In another aspect, the signal input to the hearing prosthesis is continuously and automatically classified as belonging to one of several everyday listening environments, the results of the classification being communicated to the processing means thus allowing the processing means to control the algorithm parameters.

Abstract: A hearing prosthesis that automatically adjusts itself to a surrounding listening environment by applying Hidden Markov Models is provided. In one aspect, classification results are utilized to support automatic parameter adjustment of a parameter or parameters of a predetermined signal processing algorithm executed by processing means of the hearing prosthesis. According to another aspect, features vectors extracted from a digital input signal of the hearing prosthesis and processed by the Hidden Markov Models represent substantially level and/or absolute spectrum shape independent signal features of the digital input signal. This level independent property of the extracted features vectors provides robust classification results in real-life acoustic environments.

Abstract: A hearing prosthesis that automatically adjusts itself to a surrounding listening environment by applying Hidden Markov Models is provided. In one aspect, classification results are utilized to support automatic parameter adjustment of a parameter or parameters of a predetermined signal processing algorithm executed by processing means of the hearing prosthesis. According to another aspect, features vectors extracted from a digital input signal of the hearing prosthesis and processed by the Hidden Markov Models represent substantially level and/or absolute spectrum shape independent signal features of the digital input signal. This level independent property of the extracted features vectors provides robust classification results in real-life acoustic environments.

Abstract: A hearing prosthesis that automatically adjusts itself to a surrounding listening environment is provided. In one aspect, the automatic adjustment is achieved by controlling one or several algorithm parameters of a predetermined signal processing algorithm. In another aspect, the signal input to the hearing prosthesis is continuously and automatically classified as belonging to one of several everyday listening environments, the results of the classification being communicated to the processing means thus allowing the processing means to control the algorithm parameters.