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Zeyu Ren: There are three reasons why Xiaomi is working on humanoid robots. The first reason is that we are seeing a huge decline in the labor force in China, and the world. We are working on replacing the human labor force with humanoid robots even though there is a long way to go. The second reason is that we believe humanoid robots are the most technically challenging of all robot forms. By working on humanoid robots, we can also use this technology to solve problems on other robot forms, such as quadruped robots, robotic arms, and even wheeled robots. The third reason is that Xiaomi wants to be the most technically advanced company in China, and humanoid robots are sexy.

But one day, one of our engineers who had just begun to play drums suggested that drumming may be an exception. She thought that compared to rookie drummers, humanoid robots have more advantages in hand-foot coordinated motion and rhythmic control. We all thought it was a good idea, and drumming itself is super cool and interesting. So we choose drumming to demonstrate our research.

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Ren: The most challenging part of this research was that when receiving the long sequences of drum beats, CyberOne needs to assign sequences to each arm and leg and generate continuous collision-free whole-body trajectories within the hardware constraints. So, we extract the basic beats and build our drum beat motion trajectory library offline by optimization. Then, CyberOne can generate continuous trajectories consistent with any drum score. This approach gives more freedom to CyberOne playing drums, and is only limited by the robotics capability.

Ren: Drumming requires CyberOne to coordinate whole-body motions to achieve a fast, accurate, and large range of movement. We first want to find the limit of our robot in terms of hardware and software to provide a reference for the next-generation design. Also, through this research, we have formed a complete set of automatic drumming methods for robots to perform different songs, and this experience also helps us to more quickly realize the development of other musical instruments to be played by robots.

This is actually a great way to show off the robots ability. Provided that the robot can stay in beat and hit the drums when they are supposed to be hit, would show and extremely advanced ability for the computer to manage all the actuators necessary to move the actuators so that the stick hits the drum at just the right location, speed and time.

Robotics teams from El Camino College and North Torrance High School publicly demonstrated their robot creations at the event hosted by the Robotics Club inside the Industry and Technology Education Center on Wednesday, Dec 6.

Different groups including the El Camino Robotics Club, El Camino Society of Women Engineers, North Torrance Robotics Club and North Torrance Junior Reserve Officer Training Corps (JROTC) all had representatives in attendance to demonstrate their robotic entries.

Robot Orchestra soundcloud.com/robotorchestraSpotify / iTunes / Vinyl etc: chillhop.lnk.to/FallEss2017BaWe're back with another Essentials compilation, featuring 21 Fall themed tracks from our favorite producers. As the leaves turn brown, let these tracks be the soundtrack for the shortening days. This compilation is available on all digital platforms as well as limited edition gatefold double vinyl!Thanks to everyone for the continued support.

As artificial intelligence (AI) research becomes mature, its application gets closer to public life. For example, intelligent robots are seeing various applicational scenarios, such as service robots and unmanned aerial vehicles (UAVs). Meanwhile, robotic technologies are oriented toward entertainment from practical works. Research on service-oriented robots is abundant both in and outside China, while there is relatively little research on dancing robots. Dancing to the beat might seem natural to a human, but getting robots to respond to beats requires tons of work and design.

Chronologically, Robots' applications can be segmented into several phases, from industrial robots to service robots and household robots. From the economic sector's perspective, robot applications are experienced practical->industrial-entertainment->domestic development stage. Researchers have also done many works in robotics, deep learning (DL), and music interaction in robotics. Wen (2020) designed an intelligent background music system based on DL, the internet of things (IoT), and the support vector machine (SVM). They used a recurrent neural network (RNN) structure to extract image features. Nam et al. (2019) developed an automatic string plucking system for guitar robots to generate music without machine noise. The soft robot technology was used for a new silent actuator: a soft elastic cone as a buffer to prevent impact noise. As a result, an elastic cone design method based on nonlinear finite element analysis (FEA) was proposed. The silent characteristics of the silent actuator were confirmed by the noise test that compares the silent actuator with the traditional actuator. Rajesh and Nalini (2020) represented that music was an effective medium to convey emotions. Emotional recognition in music was the process of recognizing emotions from music fragments. They proposed an instrument-like emotional recognition method in view of DL technology. The music data set was collected from strung, percussion, woodwind, and brass instruments corresponding to four emotions, namely, happiness, sadness, neutrality, and fear. From the instrumental data set, the features of Mel frequency cepstral coefficient (MFCC), normalization statistics of chroma energy, short-term Fourier Transform (FT) of chroma, spectral characteristic, spectral centroid, bandwidth, attenuation, and time characteristics were extracted. Based on the extracted features, the RNN was trained for emotional recognition. Then, the performance of RNN and baseline machine learning (ML) classification algorithm was compared. The results showed that deep RNN had an excellent effect on instrument emotional recognition. Instrument classes played an important role in music-induced emotions. Briot and Pachet (2020) indicated that in addition to traditional tasks, such as prediction, classification, and translation, DL was receiving increasing interest as a music generation method. The latest research groups, such as Google's Magenta and Spotify's Creator Technology Research Lab (CTRL), were evidenced. The motivation was to automatically use DL architecture to learn music style from any music corpus and then generate samples from the estimated distribution. Then, DL-based music generation reached certain limitations, such as feedforward in circular architecture, because they tended to imitate the learned corpus without the incentive of creativity. Besides, the DL architecture did not provide a direct method to control music generation. DL architecture automatically generated music without human-computer interaction (HCI). However, given its generated content, it still could not help musicians create and refine music. They focused on the issues of control, creativity, and interaction analysis. Then the limitations of applying DL to music generation were listed, and possible solutions were outlooked. Martin-Gutierrez et al. (2020) pointed out that the application of multimedia promoted the services provided by platforms, such as Spotify, Lastfm, or Billboard. However, the innovative methods of retrieving specific information from a large amount of music-related data have become a potential challenge in music information retrieval. They studied the creation of SpotGenTrack popular data sets. They proposed an innovative multi-mode end-to-end DL architecture HitMusicNet to predict the popularity of music recording. Experiments showed that the architecture proposed was better than the existing technology.

As in Figure 1, the music style recognition process is divided into two stages. The original signal is pre-processed in the training stage, and then the improved RNN is trained using the pre-processed data. In the testing stage, the audio to be tested is first pre-processed by simple data, and then the feature file is transformed (Mcauley et al., 2021; Wang et al., 2021).

Comparing the loss and prediction accuracy reveals that multi-parallel models have higher prediction accuracy than single ones. The recognition accuracy of the multi-parallel model reaches 79.8%, higher by 43.8% than the single LSTM model, and the model loss is only 6.85%. Overall, the recognition accuracy and loss of the AM-IndRNN reported here are optimal, reaching 88.9% and 7.48%, respectively. Therefore, the optimized LSTM has higher recognition accuracy and is more applicable for recognizing music styles and beats.

The experimental results show that compared with the model proposed by Soufineyestani et al. (2021), the AM-IndRNN reported here has a higher recognition rate on the GTZAN dataset. The experimental results of this paper are compelling. They can well complete the classification of music styles on the GTZAN dataset.

Music is our first language, and our mission is to bring love and beats to the world. We work with and support artists, technologists and visionaries of all types in order to create incredible experiences and connected moments. To create love and beats, for you.

For those that braved the rain, the playa eventually dried and the Bot rewarded us with 16 hours of music. Lee Burridge finished us off with a five hour set ending at 11am. This photo captured the smiles that endured after we cleaned the playa.

FIGURE 6. (A) Illustrative confusion matrices for the ANN showed the accuracy for classifying the 10 different song alterations during independent use and (B) while user-worn; (C) Comparison of 3 classification algorithms during independent use and with a human subject wearing the soft robotic exoskeleton. The ANN had significantly higher accuracy than the KNN and RF algorithms. ff782bc1db