Method for classifying audio data

1. A method for selecting audio data, comprising:

a pre-selection process including:

providing mood space data representative of a mood space for classifying audio data,

providing first audio data and generating a first mood space location within the mood space for the first audio data,

providing second audio data and generating a second mood space location for the second audio data, and

determining whether the second audio data is within a pre-defined neighborhood space around the first audio data by generating comparison data indicating a distance, in the mood space, between the first mood space location and the second mood space location;

a detailed comparing process including comparing, based on frequency domain related features, the first audio data and the second audio data only when the comparison data from the pre-selection process indicates the second audio data is within the pre-defined neighborhood space, wherein a plurality of other audio data are compared with respect to the first audio data according to the pre-selection process and the detailed comparing process; and

generating a play list based on the comparisons of the second audio data and the other audio data with the first audio data to include audio data thereof similar to the first audio data.

2. The method according to claim 1 , wherein the mood space is or is modeled by at least one of a Gaussian mixture model, a neural network model, or a decision tree model.

3. The method according to claim 1 , wherein the mood space is or is modeled by an N-dimensional space or manifold, and N is a given and fixed integer.

4. The method according to claim 1 , wherein the comparison data are at least one of descriptive for, representative for, or comprising at least one of a topology, a metric, a norm, and a distance defined in, or on the mood space.

5. The method according to claim 4 , wherein the comparison data or the topology, metric, norm, and the distance are obtained based on at least one of a Euclidean space model, a Gaussian mixture model, a neural network model, or a decision tree model.

6. The method according to claim 1 , wherein the mood space is defined based on Thayer's mood model.

7. The method according to claim 1 , wherein the mood space is two-dimensional and is defined based on measured or measurable entities describing happy and anxious moods and energy describing calm and energetic moods as emotional or mood parameters or attributes.

8. The method according to claim 1 , wherein the mood space is three-dimensional and is defined based on measured or measurable entities for happiness, passion, and excitement.

9. The method according to claim 1 , wherein the generated playlist consists of audio data similar to the first audio data.

10. A non-transitory computer-readable medium including executable instructions, which when executed by a processor, cause the processor to perform a method for selecting audio data, comprising:

a pre-selection process including:

providing mood space data representative of a mood space for classifying audio data,

providing first audio data and generating a first mood space location within the mood space for the first audio data,

providing second audio data and generating a second mood space location for the second audio data, and

determining whether the second audio data is within a pre-defined neighborhood space around the first audio data by generating comparison data indicating a distance, in the mood space, between the first mood space location and the second mood space location;

a detailed comparing process including comparing, based on frequency domain related features, the first audio data and the second audio data only when the comparison data from the pre-selection process indicates the second audio data is within the pre-defined neighborhood space, wherein a plurality of other audio data are compared with respect to the first audio data according to the pre-selection process and the detailed comparing process; and

generating a play list based on the comparisons of the second audio data and the other audio data with the first audio data to include audio data thereof similar to the first audio data.

11. An apparatus for selecting audio data, comprising:

means for performing a pre-selection process including:

providing mood space data representative of a mood space for classifying audio data,

providing first audio data and generating a first mood space location within the mood space for the first audio data,

providing second audio data and generating a second mood space location for the second audio data, and

determining whether the second audio data is within a pre-defined neighborhood space around the first audio data by generating comparison data indicating a distance, in the mood space, between the first mood space location and the second mood space location;

means for performing a detailed comparing process including comparing, based on frequency domain related features, the first audio data and the second audio data only when the comparison data from the pre-selection process indicates the second audio data is within the pre-defined neighborhood space, wherein a plurality of other audio data are compared with respect to the first audio data according to the pre-selection process and the detailed comparing process; and

means for generating a play list based on the comparisons of the second audio data and the other audio data with the first audio data to include audio data thereof similar to the first audio data.