Lateral approach for insertional Achilles tendinitis with Haglund deformity

ObjectiveThe study aims to investigate the functional outcome of the lateral approach for insertional Achilles tendinitis (IAT) with Haglund deformity.MethodsFrom January 2016 to September 2019, 14 cases of IAT with Haglund deformity that resisted conservative treatment received surgery in our department. A lateral approach was used to debride the bony and soft tissue and reattach the insertion of the Achilles tendon. The Visual Analog Scale (VAS), American Orthopedic Foot and Ankle Score (AOFAS), and Victorian Institute of Sport Tendon Study Group-Achilles Tendinopathy score (VISA-A) were used to evaluate clinical outcomes.ResultThe mean patient age was 39.57 years at the time of surgery. The mean follow-up was 14.74 months. The mean VAS score significantly decreased from 4.86 ± 0.86 preoperatively to 1.21 ± 1.58 postoperatively (P < 0.001). The mean AOFAS score significantly improved from 66.64 ± 6.23 preoperatively to 90.21 ± 11.50 postoperatively (P < 0.001). The mean preoperative and the last follow-up VISA-A were 66 (range 56.75–69.25) and 86 (range 75.75–97.00) points, respectively (P < 0.05).ConclusionThe lateral approach was effective and safe for IAT with Haglund deformity. Moreover, the mid-term functional outcome was promising.Level of Clinical EvidenceI

An Efficient Hidden Markov Model with Periodic Recurrent Neural Network Observer for Music Beat Tracking

In music information retrieval (MIR), beat tracking is one of the most fundamental tasks. To obtain this critical component from rhythmic music signals, a previous beat tracking system of hidden Markov model (HMM) with a recurrent neural network (RNN) observer was developed. Although the frequency of music beat is quite stable, existing HMM based methods do not take this feature into account. Accordingly, most of hidden states in these HMM-based methods are redundant, which is a disadvantage for time efficiency. In this paper, we proposed an efficient HMM using hidden states by exploiting the frequency contents of the neural network’s observation with Fourier transform, which extremely reduces the computational complexity. Observers that previous works used, such as bi-directional recurrent neural network (Bi-RNN) and temporal convolutional network (TCN), cannot perceive the frequency of music beat. To obtain more reliable frequencies from music, a periodic recurrent neural network (PRNN) based on attention mechanism is proposed as well, which is used as the observer in HMM. Experimental results on open source music datasets, such as GTZAN, Hainsworth, SMC, and Ballroom, show that our efficient HMM with PRNN is competitive to the state-of-the-art methods and has lower computational cost

An Efficient Hidden Markov Model with Periodic Recurrent Neural Network Observer for Music Beat Tracking

In music information retrieval (MIR), beat tracking is one of the most fundamental tasks. To obtain this critical component from rhythmic music signals, a previous beat tracking system of hidden Markov model (HMM) with a recurrent neural network (RNN) observer was developed. Although the frequency of music beat is quite stable, existing HMM based methods do not take this feature into account. Accordingly, most of hidden states in these HMM-based methods are redundant, which is a disadvantage for time efficiency. In this paper, we proposed an efficient HMM using hidden states by exploiting the frequency contents of the neural network’s observation with Fourier transform, which extremely reduces the computational complexity. Observers that previous works used, such as bi-directional recurrent neural network (Bi-RNN) and temporal convolutional network (TCN), cannot perceive the frequency of music beat. To obtain more reliable frequencies from music, a periodic recurrent neural network (PRNN) based on attention mechanism is proposed as well, which is used as the observer in HMM. Experimental results on open source music datasets, such as GTZAN, Hainsworth, SMC, and Ballroom, show that our efficient HMM with PRNN is competitive to the state-of-the-art methods and has lower computational cost

Transfer Learning for Music Genre Classification

Part 3: Big Data Analysis and Machine LearningInternational audienceModern music information retrieval system provides high-level features (genre, instrument, mood and so on) for searching and recommending conveniently. Among these music tags, genre is the most widely used in practice. Machine learning technique has the ability of cataloguing different genres from raw music. A disadvantage of it is that the final performance heavily depends on the used features. As a powerful learning algorithm, deep neural network can extract useful features automatically and effectively instead of time-consuming feature engineering. But deeper architecture means larger data are needed to train the neural network. In many cases, we may not have enough data to train a deep network. Transfer learning solves the problem by pre-training the network in a similar task which has enough data, then fine-tuning the parameters of the pre-trained network using the target dataset. Magnatagatune dataset is used for pre-training the proposed five-layer Recurrent Neural Network (RNN) with Gated Recurrent Unit (GRU). And in order to reduce the input of the network, scattering transform is used in this paper. Then GTZAN dataset is used as the target dataset of genre classification. Experimental results show the transfer learning way can achieve a higher average classification accuracy (95.8%) than the same deep RNN which initials the parameters randomly (93.5%). In addition, the deep RNN using transfer learning converges to the final accuracy faster than using random initialization