An audio information retrieval model based on Manifold Ranking(MR) is proposed, and ranking results are improved using a Relevance Feedback(RF) algorithm. Timbre components are employed as the model’s main feature. To compute timbre similarity, extracting the spectrum features for each frame is necessary; the large set of frames is clustered using a Gaussian Mixture Model(GMM) and expectation maximization. The typical spectra frame from GMM is drawn as data points, and MR assigns each data point a relative ranking score, which is treated as a distance instead of as traditional similarity metrics based on pair-wise distance. Furthermore, the MR algorithm can be easily generalized by adding positive and negative examples from the RF algorithm and improves the final result. Experimental results show that the proposed approach effectively improves the ranking capabilities of existing distance functions. An audio information retrieval model based on Manifold Ranking (MR) is proposed, and ranking results are improved using a Relevance Feedback (RF) algorithm. Tumbre components are employed as the model’s main feature. To compute timbre similarity, extracting the spectrum features for each frame is necessary; the large set of frames is clustered using a Gaussian Mixture Model (GMM) and expectation maximization. The typical spectra frame from GMM drawn as data points, and MR assigns each data point a relative ranking score, which is treated as a distance instead of as traditional similarity metrics based on pair-wise distance. Further, the MR algorithm can be easily generalized by adding positive and negative examples from the RF algorithm and improves the final result. Experimental results show that the draft approach effectively improves the ranking capabilities of existing distance functions.